From: Benjamin Auder <benjamin.auder@somewhere> Date: Sun, 5 Mar 2017 16:30:11 +0000 (+0100) Subject: throw away old code, prepare tests X-Git-Url: https://git.auder.net/doc/html/scripts/css/img/current/%24%7BgetWhatsApp%28link%29%7D?a=commitdiff_plain;h=62deb4244895a20a35397dfb062f0b9fe94c5012;p=epclust.git throw away old code, prepare tests --- diff --git a/README.md b/README.md index 2b103b2..f1ef56f 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,4 @@ This program takes n time-series in input and is divided in two stages: --- -The folder "communication/" contains latex sources of the slides showed at half-project(?) - -The other folder contains all the R and C code, with toy datasets (real ones are not public). +The folder ... contains ... (TODO) diff --git a/TODO b/TODO index 96a8221..f5e0015 100644 --- a/TODO +++ b/TODO @@ -1,13 +1,7 @@ simulateur : complètement aléatoire ? from Irish dataset ? +wmtsa : wavBootstrap wavelets methods in statistics with R - p180 -00-convertir2009 et 2010.R -01-extractFeatures.R pour 2009 [utilise Stbr.R] (car on prédit 2010, pas besoin) -02-cluster2009.R -03-compute-sum-of-classes2009.R -05-cluster2WER-2009.R -06-prediction.R - Essayer distance wdist du package biwavelet ? geometric structure of high dim data and dim reduction 2011 @@ -18,61 +12,14 @@ A faire: - finir les experiences (sur nb de classes, nb de curves / chunk, nb de procs) et sur d'autres architectures -dans old_C_code/build : -cmake ../stage1/src -make - -dans data/, lancer R puis : -source("../old_C_code/wrapper.R") -serialize("../old_C_code/build", "2009.csv","2009.bin",1) -library(parallel) -np = detectCores() -nbSeriesPerChunk = 3000 -nbClusters = 20 -ppam_exe("../old_C_code/build",np,"2009.bin",nbSeriesPerChunk,nbClusters) -C = getMedoids("../old_C_code/build", "ppamResult.xml", "ppamFinalSeries.bin") -first100series = deserialize("../old_C_code/build", "2009.bin", "2009.csv.part", "1-100") -distor = getDistor("../old_C_code/build", "ppamResult.xml", "2009.bin") - -- interface matrice -> binaire - OK - - - courbe synchrone - ?? - Piste à explorer pour les comparaisons: H20 renvoyer nombre d'individues par classe ? (+ somme ?) hypothèse : données déjà ordonnées 48 1/2H sur 365j utiliser du mixmod avec modèles allongés -doit toutner sur machine plutôt standard, utilisateur "lambda" +doit tourner sur machine plutôt standard, utilisateur "lambda" utiliser Rcpp ? -===== - -strategies for upscaling -From 25K to 25M : in 1000 chunks of 25K -Reference values : - K0 = 200 super consumers (SC) - Kâ = 15 nal clusters -1st strategy - Do 1000 times ONLY Energycon's 1st-step strategy on 25K clients - With the 1000 à K0 SC perform a 2-step run leading to Kâ clusters - ---> il faut lancer 1000(param: nbTasks?) tâches avec itérations (éventuelles) ---> écrire tous les résultats, puis les récupérer pour démarrer : ---> phase 2 sur 1000xK0 médoïdes - -2nd strategy - Do 1000 times Energycon's 2-step strategy on 25K clients leading to - 1000 à Kâ intermediate clusters - Treat the intermediate clusters as individual curves and perform a - single 2-step run to get Kâ final clusters - ---> 1000(nbTasks) tâches avec itérations possibles, puis phase 2 en fin de chaqune des 1000 -tâches. On obtient 1000xK* médoïdes ---> Phase 2 sur les 1000xK* médoïdes - #point avec Jairo: #rentrer dans code C cwt continue Rwave #passer partie sowas à C diff --git a/epclust/DESCRIPTION b/epclust/DESCRIPTION index bb3e6cf..aa5acde 100644 --- a/epclust/DESCRIPTION +++ b/epclust/DESCRIPTION @@ -7,7 +7,9 @@ Description: EPCLUST: Electric Power curves CLUSTering, through their wavelets d Version: 0.1-0 Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre], - Jairo Cugliari <Jairo.Cugliari@univ-lyon2.fr> [aut] + Jairo Cugliari <Jairo.Cugliari@univ-lyon2.fr> [aut], + Yannig Goude <Yannig.Goude@edf.fr> [ctb], + Jean-Michel Poggi <Jean-Michel.Poggi@math.u-psud.fr> [ctb] Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> Depends: R (>= 3.0.0), @@ -16,7 +18,6 @@ Depends: wavelets Suggests: testthat, - knitr, wmtsa, RSQLite License: MIT + file LICENSE diff --git a/epclust/LICENSE b/epclust/LICENSE index 08526c5..434f922 100644 --- a/epclust/LICENSE +++ b/epclust/LICENSE @@ -1,4 +1,7 @@ -Copyright (c) 2016-2017, Jairo Cugliari ; 2016-2017, Benjamin Auder +Copyright (c) 2016-2017, Benjamin Auder + 2016-2017, Jairo Cugliari + 2016-2017, Yannig Goude + 2016-2017, Jean-Michel Poggi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the diff --git a/epclust/README.md b/epclust/README.md deleted file mode 100644 index cfe894e..0000000 --- a/epclust/README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Clustering individual power curves using wavelets decomposition - -This package was funded by a contract with EDF; its goal is to identify groups of customers. - -## Description - -TODO - -## Example - -TODO diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R deleted file mode 100644 index eeed576..0000000 --- a/epclust/tests/testthat/test.clustering.R +++ /dev/null @@ -1,2 +0,0 @@ -computeClusters -computeSynchrones diff --git a/epclust/tests/testthat/test.clusteringTask.R b/epclust/tests/testthat/test.clusteringTask.R new file mode 100644 index 0000000..f45276c --- /dev/null +++ b/epclust/tests/testthat/test.clusteringTask.R @@ -0,0 +1,38 @@ +computeClusters +computeSynchrones + +# Cluster one full task (nb_curves / ntasks series) +clusteringTask = function(indices,getSeries,getSeriesForSynchrones,synchrones_file, + getCoefs,K1,K2,nb_series_per_chunk,ncores,to_file,ftype) +{ + cl = parallel::makeCluster(ncores) + repeat + { + nb_workers = max( 1, round( length(indices) / nb_series_per_chunk ) ) + indices_workers = lapply(seq_len(nb_workers), function(i) { + upper_bound = ifelse( i<nb_workers, + min(nb_series_per_chunk*i,length(indices)), length(indices) ) + indices[(nb_series_per_chunk*(i-1)+1):upper_bound] + }) + indices = unlist( parallel::parLapply(cl, indices_workers, function(inds) + computeClusters1(inds, getCoefs, K1)) ) + if (length(indices_clust) == K1) + break + } + parallel::stopCluster(cl) + if (K2 == 0) + return (indices) + computeClusters2(indices, K2, getSeries, getSeriesForSynchrones, to_file, + nb_series_per_chunk,ftype) + vector("integer",0) +} + +# Apply the clustering algorithm (PAM) on a coeffs or distances matrix +computeClusters1 = function(indices, getCoefs, K1) +{ + coefs = getCoefs(indices) + indices[ cluster::pam(coefs, K1, diss=FALSE)$id.med ] +} + +# Cluster a chunk of series inside one task (~max nb_series_per_chunk) +computeClusters2 = function(indices, K2, getSeries, getSeriesForSynchrones, to_file, diff --git a/old_C_code/stage2_UNFINISHED/src/.gitkeep b/epclust/tests/testthat/test.computeCluster.R similarity index 100% rename from old_C_code/stage2_UNFINISHED/src/.gitkeep rename to epclust/tests/testthat/test.computeCluster.R diff --git a/epclust/tests/testthat/test.de_serialize.R b/epclust/tests/testthat/test.de_serialize.R new file mode 100644 index 0000000..41c57e4 --- /dev/null +++ b/epclust/tests/testthat/test.de_serialize.R @@ -0,0 +1,52 @@ + +#data: matrix of double or connection +serialize = function(data, file, type, nb_per_chunk) +{ + bin_data = file(file, "ab") + #write data length on first call + nbytes = ifelse(type=="double",8,4) + first_write = FALSE + if (file.info(file)$size == 0) + { + #number of items always on 8 bytes + writeBin(0L, bin_data, size=8) #,endian="little") + first_write = TRUE + } + if (is.matrix(data)) + { + writeBin(t(data), bin_data, size=nbytes) + data_length = ncol(data) + } + else #if (is(data, "connection")) + { + if (first_write) + { + data_line = scan(data, double(), sep=",", nlines=1) + writeBin(data_line, bin_data, size=nbytes) + data_length = length(data_line) + } + repeat + { + data_chunk = scan(data, double(), sep=",", nlines=nb_per_chunk) + if (length(data_chunk)==0) + break + writeBin(data_chunk, bin_data, size=nbytes) + } + } + if (first_write) + { + #ecrire file_size-1 / (nbytes*nbWritten) en 0 dans bin_data ! ignored == file_size + ignored = seek(bin_data, 0) + writeBin(data_length, bin_data, size=8) + } + close(bin_data) +} + +#TODO: read in binary file, always same structure +getDataFromFile(indices, file, type) +{ + bin_data = file(file, "rb") + nbytes = ifelse(type=="double",8,4) + data_length = readBin(bin_data,"double",1,nbytes) #,endian="little") + t(sapply(indices, function(i) readBin(bin_data,"double",n=data_length,size=nbytes))) +} diff --git a/epclust/tests/testthat/test.utils.R b/epclust/tests/testthat/test.utils.R deleted file mode 100644 index d228338..0000000 --- a/epclust/tests/testthat/test.utils.R +++ /dev/null @@ -1,3 +0,0 @@ -serialize -deserialize -getCoeffs diff --git a/initialize.sh b/initialize.sh index 0b79c37..8264860 100755 --- a/initialize.sh +++ b/initialize.sh @@ -4,7 +4,7 @@ git submodule init && git submodule update --merge #filter for git-fat echo -e '*.pdf filter=fat\n*.tar.xz filter=fat' > .gitattributes -#pre-push hook: git fat push, submodules update +#pre-push hook: git fat pull/push, submodules update echo -e '#!/bin/sh\n./.git-fat/git-fat pull\n./.git-fat/git-fat push\ngit submodule update --merge' > .git/hooks/pre-push chmod 755 .git/hooks/pre-push #.gitfat file with remote on gitfat@auder.net diff --git a/old_C_code/.gitignore b/old_C_code/.gitignore deleted file mode 100644 index 1ebda79..0000000 --- a/old_C_code/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -#ignore building files and folders -build/ diff --git a/old_C_code/README b/old_C_code/README deleted file mode 100644 index 6a539af..0000000 --- a/old_C_code/README +++ /dev/null @@ -1,54 +0,0 @@ -0) Download & compile Benjamin's specific library - - git clone git@auder.net:cgds - cd cgds - bash makeMakefile.sh src - make src - sudo make install - -Make sure that the install destination is on the LD_LIBRARY_PATH environment variable. - -1) Compile source code for 1st stage clustering - - mkdir -p build/stage1/src - cd build/stage1/src - cmake ../../../stage1/src - make - -2) #repeat previous lines for stage 2 ??? - - -NOTA: Need to have openmpi, mpich (compiler for mpi) libxml, and libgsl installed. - - -Usage (stage 1) : - -Serialize input data using - - ppam.exe serialize inputfile_edf outputfile_edf 1 0 - -# 1 indicates data is by column -# 0 means process all the rows - - mpirun -np nbProcess ppam.exe cluster ifilename nbSeriesInChunk nbClusters randomize p_for_dissims - -## ex. > mpirun -np 4 ./ppam.exe cluster ~/tmp/2009.bin 5000 200 1 2 - -Where : - nbProcess = number of simultaneous processes - ifilename = path to serialized dataset (read below) - nbSeriesInChunk = number of time-series to process sequentially - nbClusters = number of clusters - randomize = 1 to dispatch time-series at random. 0 to process them in order - p_for_dissims = the 'p' of L_p distance used to compute dissimilarities - - -The results are stored in ppamResult.xml (curves ids and ranks) while ppamFinalSeries.bin -are the curves used in the last clustering step. The ranks in ppamResult.xml refer to the -curves in ppamFinalSeries.bin - - -Note : custom [de]serialization. Consider writing your own -in src/TimeSeries/ folder if you plan to test the package. - -See also src/main.c for the details. diff --git a/old_C_code/emergency_mode b/old_C_code/emergency_mode deleted file mode 100644 index 92c1cd0..0000000 --- a/old_C_code/emergency_mode +++ /dev/null @@ -1,12 +0,0 @@ -#seems that current code doesn't work great (deserialize is broken somewhere). -#So, this allow to use old code as it worked for JdS slides (e.g.): - -git reset --hard 16da38ab3419e7063ce367eb878a9e60d2... - -cd code/stage1/build -cmake ../src -make - -cd ../data -../build/ppam.exe serialize 2011.csv 2011.bin 1 0 -mpirun -np 4 ../build/ppam.exe cluster 2011.bin 3000 20 1 2 diff --git a/old_C_code/stage1/src/Algorithm/compute_coefficients.c b/old_C_code/stage1/src/Algorithm/compute_coefficients.c deleted file mode 100644 index cfec0e6..0000000 --- a/old_C_code/stage1/src/Algorithm/compute_coefficients.c +++ /dev/null @@ -1,57 +0,0 @@ -#include <stdlib.h> -#include "Util/types.h" -#include <math.h> -#include <gsl/gsl_wavelet.h> -#include <gsl/gsl_spline.h> - -// compute rows of the matrix of reduced coordinates -void compute_coefficients(PowerCurve* powerCurves, uint32_t nbSeries, uint32_t nbValues, - float* reducedCoordinates, uint32_t index, uint32_t nbReducedCoordinates) -{ - uint32_t D = (1 << nbReducedCoordinates); - double* x = (double*) malloc(nbValues*sizeof(double)); - for (uint32_t i=0; i<nbValues; i++) - x[i] = i; - double* y = (double*) malloc(nbValues*sizeof(double)); - double* interpolatedTranformedCurve = (double*) malloc(D*sizeof(double)); - gsl_interp* linearInterpolation = gsl_interp_alloc(gsl_interp_linear, nbValues); - gsl_interp_accel* acc = gsl_interp_accel_alloc(); - gsl_wavelet_workspace* work = gsl_wavelet_workspace_alloc(D); - //gsl_wavelet* w = gsl_wavelet_alloc(gsl_wavelet_bspline, 206); //used for starlight - gsl_wavelet* w = gsl_wavelet_alloc(gsl_wavelet_daubechies, 6); //used for power curves - //gsl_wavelet* w = gsl_wavelet_alloc(gsl_wavelet_haar, 2); - for (uint32_t i = 0; i < nbSeries; i++) - { - //Spline interpolation to have D = 2^u sample points - for (uint32_t j=0; j<nbValues; j++) - y[j] = powerCurves[i].values[j]; - gsl_interp_init(linearInterpolation, x, y, nbValues); - for (uint32_t j=0; j<D; j++) - { - interpolatedTranformedCurve[j] = - gsl_interp_eval(linearInterpolation, x, y, j*((float)(nbValues-1)/(D-1)), acc); - } - //DWT transform (in place) on interpolated curve [TODO: clarify stride parameter] - gsl_wavelet_transform_forward(w, interpolatedTranformedCurve, 1, D, work); - //Fill reducedCoordinates with energy contributions - uint32_t t0 = 1; - uint32_t t1 = 1; - for (uint32_t j=0; j<nbReducedCoordinates; j++) - { - t1 += (1 << j); - //reducedCoordinates[(index+i)*nbReducedCoordinates+j] = sqrt( sum( x[t0:t1]^2 ) ) - float sumOnSegment = 0.0; - for (uint32_t u=t0; u<t1; u++) - sumOnSegment += interpolatedTranformedCurve[u]*interpolatedTranformedCurve[u]; - reducedCoordinates[(index+i)*nbReducedCoordinates+j] = sqrt(sumOnSegment); - t0 = t1; - } - } - gsl_interp_free(linearInterpolation); - gsl_interp_accel_free(acc); - free(x); - free(y); - free(interpolatedTranformedCurve); - gsl_wavelet_free(w); - gsl_wavelet_workspace_free(work); -} diff --git a/old_C_code/stage1/src/Algorithm/compute_coefficients.h b/old_C_code/stage1/src/Algorithm/compute_coefficients.h deleted file mode 100644 index a77dc60..0000000 --- a/old_C_code/stage1/src/Algorithm/compute_coefficients.h +++ /dev/null @@ -1,10 +0,0 @@ -#ifndef PPAM_COMPUTE_COEFFICIENTS_H -#define PPAM_COMPUTE_COEFFICIENTS_H - -#include "Util/types.h" - -// compute rows of the matrix of reduced coordinates (see computeCoefficients.R) -void compute_coefficients(PowerCurve* powerCurves, uint32_t nbSeries, uint32_t nbValues, - float* reducedCoordinates, uint32_t index, uint32_t nbReducedCoordinates); - -#endif diff --git a/old_C_code/stage1/src/Algorithm/get_dissimilarities.c b/old_C_code/stage1/src/Algorithm/get_dissimilarities.c deleted file mode 100644 index 5dc8ea2..0000000 --- a/old_C_code/stage1/src/Algorithm/get_dissimilarities.c +++ /dev/null @@ -1,47 +0,0 @@ -#include "Util/types.h" -#include <stdlib.h> -#include <math.h> - -// compute L^p dissimilarities for a nxm matrix -float* get_dissimilarities_intra(float* samples, uint32_t nbSamples, uint32_t nbValues, uint32_t p) -{ - float* dissimilarities = (float*) malloc(nbSamples*nbSamples*sizeof(float)); - for (uint32_t i=0; i<nbSamples; i++) - { - dissimilarities[i*nbSamples+i] = 0.0; - for (uint32_t j=0; j<i; j++) - { - // dissimilarities[i*nbSamples+j] = L^p distance between reduced rows i and j - double dissim = 0.0; - for (uint32_t m=0; m<nbValues; m++) - { - double delta = fabs(samples[i*nbValues+m] - samples[j*nbValues+m]); - dissim += pow(delta, p); - } - dissimilarities[i*nbSamples+j] = pow(dissim, 1.0/p); - dissimilarities[j*nbSamples+i] = dissimilarities[i*nbSamples+j]; - } - } - return dissimilarities; -} - -// compute L^p dissimilarities between rows of 2 matrices -float* get_dissimilarities_inter(float* mat1, uint32_t n1, float* mat2, uint32_t n2, - uint32_t nbValues, uint32_t p) -{ - float* dissimilarities = (float*) malloc(n1*n2*sizeof(float)); - for (uint32_t i=0; i<n1; i++) - { - for (uint32_t j=0; j<n2; j++) - { - double dissim = 0.0; - for (uint32_t m=0; m<nbValues; m++) - { - double delta = fabs(mat1[i*nbValues+m] - mat2[j*nbValues+m]); - dissim += pow(delta, p); - } - dissimilarities[i*n2+j] = pow(dissim, 1.0/p); - } - } - return dissimilarities; -} diff --git a/old_C_code/stage1/src/Algorithm/get_dissimilarities.h b/old_C_code/stage1/src/Algorithm/get_dissimilarities.h deleted file mode 100644 index b9580de..0000000 --- a/old_C_code/stage1/src/Algorithm/get_dissimilarities.h +++ /dev/null @@ -1,13 +0,0 @@ -#ifndef PPAM_GET_DISSIMILARITIES_H -#define PPAM_GET_DISSIMILARITIES_H - -#include "Util/types.h" - -// compute L^p dissimilarities for a nxm matrix -float* get_dissimilarities_intra(float* samples, uint32_t nbSamples, uint32_t nbValues, uint32_t p); - -// compute L^p dissimilarities between rows of 2 matrices -float* get_dissimilarities_inter(float* mat1, uint32_t n1, float* mat2, uint32_t n2, - uint32_t nbValues, uint32_t p); - -#endif diff --git a/old_C_code/stage1/src/Algorithm/pam.c b/old_C_code/stage1/src/Algorithm/pam.c deleted file mode 100644 index 60e3efe..0000000 --- a/old_C_code/stage1/src/Algorithm/pam.c +++ /dev/null @@ -1,167 +0,0 @@ -#include "Util/types.h" -#include "Util/rng.h" -#include <cgds/Vector.h> -#include <stdlib.h> -#include <math.h> - -// auxiliary function to obtain a random sample of 1..n with k elements -static void sample(uint32_t* v, uint32_t n, uint32_t k) -{ - // refVect = (0,1,...,n-1,n) - uint32_t* refVect = (uint32_t*) malloc(n * sizeof(uint32_t)); - for (uint32_t i = 0; i < n; i++) - refVect[i] = i; - - uint32_t curSize = n; // current size of the sampling set - for (uint32_t j = 0; j < k; j++) - { - // pick an index in sampling set: - uint32_t index = get_rand_int() % curSize; - v[j] = refVect[index]; - // move this index outside of sampling set: - refVect[index] = refVect[--curSize]; - } - - free(refVect); -} - -// assign a vector (represented by its dissimilarities to others, as dissimilarities[index,]) -// to a cluster, represented by its center ==> output is integer in 0..K-1 -static uint32_t assignCluster(uint32_t index, float* dissimilarities, - uint32_t* centers, uint32_t n, uint32_t K) -{ - uint32_t minIndex = 0; - float minDist = dissimilarities[index * n + centers[0]]; - - for (uint32_t j = 1; j < K; j++) - { - if (dissimilarities[index * n + centers[j]] < minDist) - { - minDist = dissimilarities[index * n + centers[j]]; - minIndex = j; - } - } - - return minIndex; -} - -// assign centers given a clustering, and also compute corresponding distortion -static void assign_centers(uint32_t nbClusters, Vector** clusters, float* dissimilarities, - uint32_t nbItems, uint32_t* ctrs, float* distor) -{ - *distor = 0.0; - // TODO [heuristic]: checking only a neighborhood of the former center ? - for (uint32_t j = 0; j < nbClusters; j++) - { - // If the cluster is empty, choose a center at random (pathological case...) - uint32_t minIndex = get_rand_int() % nbItems; - float minSumDist = INFINITY; - for (uint32_t i = 0; i < vector_size(clusters[j]); i++) - { - uint32_t index1; - vector_get(clusters[j], i, index1); - // attempt to use current index as center - float sumDist = 0.0; - for (uint32_t ii = 0; ii < vector_size(clusters[j]); ii++) - { - uint32_t index2; - vector_get(clusters[j], ii, index2); - sumDist += dissimilarities[index1 * nbItems + index2]; - } - if (sumDist < minSumDist) - { - minSumDist = sumDist; - minIndex = index1; - } - } - ctrs[j] = minIndex; - *distor += minSumDist; - } -} - -// Core PAM algorithm from a dissimilarity matrix; (e.g. nstart=10, maxiter=100) -void pam(float* dissimilarities, uint32_t nbItems, uint32_t nbClusters, int clustOnMedoids, - uint32_t nbStart, uint32_t maxNbIter, Result_t* result) -{ - uint32_t* ctrs = result->medoids_ranks; //shorthand - uint32_t* oldCtrs = (uint32_t*) malloc(nbClusters * sizeof(uint32_t)); - Vector** clusters = (Vector**) malloc(nbClusters * sizeof(Vector*)); - Vector** bestClusts = (Vector**) malloc(nbClusters * sizeof(Vector*)); - for (uint32_t j = 0; j < nbClusters; j++) - { - clusters[j] = vector_new(uint32_t); - bestClusts[j] = vector_new(uint32_t); - } - - float lastDistor, distor, bestDistor = INFINITY; - for (uint32_t startKount = 0; startKount < nbStart; startKount++) - { - // centers (random) [re]initialization - if (clustOnMedoids) - { - // In this special case (clustering groups of medoids), we can improve the sampling - uint32_t randomMedoidRank = get_rand_int() % (nbItems / nbClusters); - uint32_t startIndex = randomMedoidRank * nbClusters; - for (uint32_t index = startIndex; index < startIndex + nbClusters; index++) - ctrs[index - startIndex] = index; - } - else - // No information: complete random sampling - sample(ctrs, nbItems, nbClusters); - for (uint32_t j = 0; j < nbClusters; j++) - oldCtrs[j] = 0; - uint32_t kounter = 0; - - distor = INFINITY; - do // while 'distortion' decreases... - { - // (re)initialize clusters to empty sets - for (uint32_t j = 0; j < nbClusters; j++) - vector_clear(clusters[j]); - - // estimate clusters belongings - for (uint32_t i = 0; i < nbItems; i++) - { - uint32_t affectation = assignCluster(i, dissimilarities, ctrs, nbItems, nbClusters); - vector_push(clusters[affectation], i); - } - - // copy current centers to old centers - for (uint32_t j = 0; j < nbClusters; j++) - oldCtrs[j] = ctrs[j]; - - // recompute centers - lastDistor = distor; - assign_centers(nbClusters, clusters, dissimilarities, nbItems, ctrs, &distor); - } - while (distor < lastDistor && kounter++ < maxNbIter); - - if (distor < bestDistor) - { - // copy current clusters into bestClusts - for (uint32_t j = 0; j < nbClusters; j++) - { - vector_clear(bestClusts[j]); - for (uint32_t i = 0; i < vector_size(clusters[j]); i++) - { - uint32_t index; - vector_get(clusters[j], i, index); - vector_push(bestClusts[j], index); - } - } - bestDistor = distor; - } - } - - // Assign centers from bestClusts - assign_centers(nbClusters, bestClusts, dissimilarities, nbItems, ctrs, &distor); - - free(oldCtrs); - for (uint32_t j = 0; j < nbClusters; j++) - { - vector_destroy(clusters[j]); - vector_destroy(bestClusts[j]); - } - free(clusters); - free(bestClusts); -} diff --git a/old_C_code/stage1/src/Algorithm/pam.h b/old_C_code/stage1/src/Algorithm/pam.h deleted file mode 100644 index 353759c..0000000 --- a/old_C_code/stage1/src/Algorithm/pam.h +++ /dev/null @@ -1,13 +0,0 @@ -#ifndef PPAM_PAM_H -#define PPAM_PAM_H - -#include "Util/types.h" - -#define NSTART 5 -#define MAXITER 100 - -// Core PAM algorithm from a 'flat' dissimilarity matrix -void pam(float* dissimilarities, uint32_t nbItems, uint32_t nbClusters, - int clustOnMedoids, uint32_t nbStart, uint32_t maxNbIter, Result_t* result); - -#endif diff --git a/old_C_code/stage1/src/CMakeLists.txt b/old_C_code/stage1/src/CMakeLists.txt deleted file mode 100644 index b69602f..0000000 --- a/old_C_code/stage1/src/CMakeLists.txt +++ /dev/null @@ -1,48 +0,0 @@ -cmake_minimum_required(VERSION 2.8.5) -project(ppam_mpi) - -set(CMAKE_C_FLAGS "-std=gnu99") -set(CMAKE_C_FLAGS_DEBUG "-g3") -set(CMAKE_C_FLAGS_RELEASE "-O3") - -#already by default... ?! -include_directories("${CMAKE_SOURCE_DIR}") - -#ppam executable (serialize, cluster, classif) - -#cds -set(CGDS_INCLUDE_PATH "/usr/local/include/cgds") -#set(CGDS_LIBRARY "/usr/local/lib/libcgds.so") -#find_path(CDS_INCLUDE_PATH "cgds/cgds.h") -#find_library(CGDS_LIBRARY cgds) -include_directories(${CGDS_INCLUDE_PATH}) - -#GSL: standard location -find_path(GSL_INCLUDE_PATH "gsl/gsl_linalg.h") -include_directories(${GSL_INCLUDE_PATH}) -find_library(GSL_LIBRARY gsl) -find_library(GSLCBLAS_LIBRARY gslcblas) -set(GSL_LIBRARIES ${GSL_LIBRARY} ${GSLCBLAS_LIBRARY}) - -#MPI: standard location -find_package(MPI REQUIRED) -include_directories(${MPI_INCLUDE_PATH}) - -#LibXml2: standard location -find_package(LibXml2 REQUIRED) -include_directories(${LIBXML2_INCLUDE_DIR}) - -file(GLOB_RECURSE ppam_src "${CMAKE_SOURCE_DIR}/*.c") -add_executable(ppam.exe ${ppam_src}) -add_library(ppam SHARED ${ppam_src}) -target_link_libraries(ppam.exe m cgds ${MPI_LIBRARIES} ${LIBXML2_LIBRARIES} ${GSL_LIBRARIES})# ${CGDS_LIBRARY}) -target_link_libraries(ppam m cgds ${MPI_LIBRARIES} ${LIBXML2_LIBRARIES} ${GSL_LIBRARIES})# ${CGDS_LIBRARY}) - -if (MPI_COMPILE_FLAGS) - set_target_properties(ppam.exe PROPERTIES - COMPILE_FLAGS "${MPI_COMPILE_FLAGS}") -endif() -if (MPI_LINK_FLAGS) - set_target_properties(ppam.exe PROPERTIES - LINK_FLAGS "${MPI_LINK_FLAGS}") -endif() diff --git a/old_C_code/stage1/src/Classification/_Readme b/old_C_code/stage1/src/Classification/_Readme deleted file mode 100644 index 4e34128..0000000 --- a/old_C_code/stage1/src/Classification/_Readme +++ /dev/null @@ -1,2 +0,0 @@ -The output of a k-style clustering can easily be used for supervised classification: -we assign any new observation to the closest cluster representative ("center"). diff --git a/old_C_code/stage1/src/Classification/getClass.c b/old_C_code/stage1/src/Classification/getClass.c deleted file mode 100644 index acec167..0000000 --- a/old_C_code/stage1/src/Classification/getClass.c +++ /dev/null @@ -1,46 +0,0 @@ -#include "Algorithm/get_dissimilarities.h" -#include "TimeSeries/deserialize.h" -#include <math.h> -#include "Algorithm/compute_coefficients.h" -#include <string.h> -#include "Util/utils.h" - -uint32_t* get_class(PowerCurve* data, uint32_t nbSeries, PowerCurve* medoids, - uint32_t nbClusters, uint32_t nbValues, uint32_t p_for_dissims, double* DISTOR) -{ - // nbReducedCoordinates = smallest power of 2 which is above nbValues - uint32_t nbReducedCoordinates = (uint32_t)ceil(log2(nbValues)); - - // Preprocessing to reduce dimension of both data and medoids - float* reducedCoordinates_data = (float*) malloc(nbSeries * nbReducedCoordinates * sizeof(float)); - compute_coefficients(data, nbSeries, nbValues, - reducedCoordinates_data, 0, nbReducedCoordinates); - float* reducedCoordinates_medoids = (float*) malloc(nbClusters * nbReducedCoordinates * sizeof(float)); - compute_coefficients(medoids, nbClusters, nbValues, - reducedCoordinates_medoids, 0, nbReducedCoordinates); - - float* dissimilarities = get_dissimilarities_inter(reducedCoordinates_data, nbSeries, - reducedCoordinates_medoids, nbClusters, nbReducedCoordinates, p_for_dissims); - free(reducedCoordinates_data); - free(reducedCoordinates_medoids); - - // 3] Finally, assign each row to the least dissimilar center - uint32_t* result = (uint32_t*) malloc(nbSeries*sizeof(uint32_t)); - for (uint32_t i=0; i<nbSeries; i++) - { - uint32_t minIndex = 0; - float minDissim = dissimilarities[i*nbClusters + 0]; - for (uint32_t j=1; j<nbClusters; j++) - { - if (dissimilarities[i*nbClusters + j] < minDissim) - { - minDissim = dissimilarities[i*nbClusters + j]; - minIndex = j; - } - } - result[i] = minIndex + 1; - (*DISTOR) += minDissim; - } - free(dissimilarities); - return result; -} diff --git a/old_C_code/stage1/src/Classification/getClass.h b/old_C_code/stage1/src/Classification/getClass.h deleted file mode 100644 index 4bb4af1..0000000 --- a/old_C_code/stage1/src/Classification/getClass.h +++ /dev/null @@ -1,9 +0,0 @@ -#ifndef PPAM_GET_CLASS_H -#define PPAM_GET_CLASS_H - -#include "Util/types.h" - -uint32_t* get_class(PowerCurve* data, uint32_t nbSeries, PowerCurve* medoids, - uint32_t nbClusters, uint32_t nbValues, uint32_t p_for_dissims, double* DISTOR); - -#endif diff --git a/old_C_code/stage1/src/MPI_Communication/pack.c b/old_C_code/stage1/src/MPI_Communication/pack.c deleted file mode 100644 index 8cb7b4f..0000000 --- a/old_C_code/stage1/src/MPI_Communication/pack.c +++ /dev/null @@ -1,60 +0,0 @@ -#include <stdlib.h> -#include "Util/types.h" -#include "Util/utils.h" - -// serialize a Work_t object into a bytes string -void pack_work(Work_t* work, uint32_t nbSeriesInChunk, Byte* packedWork) -{ - uint32_t index = 0; - - while (work->inputFileName[index] != 0) - { - packedWork[index] = work->inputFileName[index]; - index++; - } - // complete with zeros - while (index < NCHAR_FNAME) - packedWork[index++] = 0; - - write_int(work->nbSeries, packedWork + index); - index += 4; - - for (uint32_t i = 0; i < work->nbSeries; i++) - { - write_int(work->ranks[i], packedWork + index); - index += 4; - } - // complete with zeros - for (uint32_t i = 0; i < nbSeriesInChunk - work->nbSeries; i++) - { - write_int(0, packedWork + index); - index += 4; - } - - write_int(work->nbClusters, packedWork + index); - index += 4; - write_int(work->clustOnMedoids, packedWork + index); - index += 4; - write_int(work->p_for_dissims, packedWork + index); -} - -// serialize a Result_t object into a bytes string -void pack_result(Result_t* result, Byte* packedResult) -{ - uint32_t index = 0; - - write_int(result->nbClusters, packedResult); - index += 4; - - for (uint32_t i = 0; i < result->nbClusters; i++) - { - write_int(result->medoids_ID[i], packedResult + index); - index += 4; - } - - for (uint32_t i = 0; i < result->nbClusters; i++) - { - write_int(result->medoids_ranks[i], packedResult + index); - index += 4; - } -} diff --git a/old_C_code/stage1/src/MPI_Communication/pack.h b/old_C_code/stage1/src/MPI_Communication/pack.h deleted file mode 100644 index 38338f7..0000000 --- a/old_C_code/stage1/src/MPI_Communication/pack.h +++ /dev/null @@ -1,12 +0,0 @@ -#ifndef PPAM_PACK_H -#define PPAM_PACK_H - -#include "Util/types.h" - -// serialize a Work_t object into a bytes string -void pack_work(Work_t* work, uint32_t nbSeriesInChunk, Byte* packedWork); - -// serialize a Result_t object into a bytes string -void pack_result(Result_t* result, Byte* packedResult); - -#endif diff --git a/old_C_code/stage1/src/MPI_Communication/unpack.c b/old_C_code/stage1/src/MPI_Communication/unpack.c deleted file mode 100644 index c0f7e3c..0000000 --- a/old_C_code/stage1/src/MPI_Communication/unpack.c +++ /dev/null @@ -1,68 +0,0 @@ -#include <stdlib.h> -#include "Util/types.h" -#include "Util/utils.h" - -// deserialize a Work_t object from a bytes string -Work_t* unpack_work(Byte* packedWork, uint32_t nbSeriesInChunk) -{ - Work_t* work = (Work_t*) malloc(sizeof(Work_t)); - - uint32_t index = 0; - - size_t inputFileNameSize = 0; - while (packedWork[inputFileNameSize++] != 0) { } - work->inputFileName = (char*) malloc(inputFileNameSize); - while (index < inputFileNameSize) - { - work->inputFileName[index] = packedWork[index]; - index++; - } - - index = NCHAR_FNAME; - - uint32_t nbSeries = work->nbSeries = bInt_to_uint(packedWork + index); - index += 4; - - work->ranks = (uint32_t*) malloc(nbSeries * sizeof(uint32_t)); - for (uint32_t i = 0; i < nbSeries; i++) - { - work->ranks[i] = bInt_to_uint(packedWork + index); - index += 4; - } - // shift over the zeros - index += 4 * (nbSeriesInChunk - nbSeries); - - work->nbClusters = bInt_to_uint(packedWork + index); - index += 4; - work->clustOnMedoids = bInt_to_uint(packedWork + index); - index += 4; - work->p_for_dissims = bInt_to_uint(packedWork + index); - - return work; -} - -// deserialize a Result_t object from a bytes string -Result_t* unpack_result(Byte* packedResult) -{ - Result_t* result = (Result_t*) malloc(sizeof(Result_t)); - uint32_t index = 0; - - uint32_t nbClusters = result->nbClusters = bInt_to_uint(packedResult); - index += 4; - - result->medoids_ID = (uint32_t*) malloc(nbClusters * sizeof(uint32_t)); - for (uint32_t i = 0; i < nbClusters; i++) - { - result->medoids_ID[i] = bInt_to_uint(packedResult + index); - index += 4; - } - - result->medoids_ranks = (uint32_t*) malloc(nbClusters * sizeof(uint32_t)); - for (uint32_t i = 0; i < nbClusters; i++) - { - result->medoids_ranks[i] = bInt_to_uint(packedResult + index); - index += 4; - } - - return result; -} diff --git a/old_C_code/stage1/src/MPI_Communication/unpack.h b/old_C_code/stage1/src/MPI_Communication/unpack.h deleted file mode 100644 index 3c7b414..0000000 --- a/old_C_code/stage1/src/MPI_Communication/unpack.h +++ /dev/null @@ -1,12 +0,0 @@ -#ifndef PPAM_UNPACK_H -#define PPAM_UNPACK_H - -#include "Util/types.h" - -// deserialize a Work_t object from a bytes string -Work_t* unpack_work(Byte* packedWork, uint32_t nbSeriesInChunk); - -// deserialize a Result_t object from a bytes string -Result_t* unpack_result(Byte* packedResult); - -#endif diff --git a/old_C_code/stage1/src/MPI_Main/master.c b/old_C_code/stage1/src/MPI_Main/master.c deleted file mode 100644 index dfecfde..0000000 --- a/old_C_code/stage1/src/MPI_Main/master.c +++ /dev/null @@ -1,383 +0,0 @@ -#include "Util/types.h" -#include "Util/utils.h" -#include <mpi.h> -#include <stdlib.h> -#include <stdio.h> -#include <string.h> -#include <math.h> -#include <unistd.h> -#include "MPI_Communication/pack.h" -#include "MPI_Communication/unpack.h" -#include "Util/rng.h" - -// save the final result in XML format -static void result_to_XML(Result_t* result, const char* inputFileName, - const char* lastBinaryFileName, uint32_t p_for_dissims) -{ - uint32_t nbClusters = result->nbClusters; - FILE* ofile = fopen("ppamResult.xml", "w"); - - fprintf(ofile, "<medoids>\n\n"); - - fprintf(ofile, " <file>"); - fprintf(ofile, "%s", lastBinaryFileName); - fprintf(ofile, "</file>\n\n"); - - fprintf(ofile, " <p_for_dissims>"); - fprintf(ofile, "%u", p_for_dissims); - fprintf(ofile, "</p_for_dissims>\n\n"); - - fprintf(ofile, " <IDs>\n"); - for (uint32_t i=0; i<nbClusters; i++) - fprintf(ofile, " <ID>%u</ID>\n", result->medoids_ID[i]); - fprintf(ofile, " </IDs>\n\n"); - - // medoids ranks in last binary file (human printing: 0 --> 1 ...etc) - fprintf(ofile, " <ranks>\n"); - for (uint32_t i=0; i<nbClusters; i++) - fprintf(ofile, " <rank>%u</rank>\n", result->medoids_ranks[i] + 1); - fprintf(ofile, " </ranks>\n\n"); - - fprintf(ofile, "</medoids>\n"); - fclose(ofile); -} - -static void binaryResult_to_file(Result_t* result, const char* inputFileName, - const char* outputFileName) -{ - // Determine tsLength from inputFile - uint32_t tsLength = get_tsLength(inputFileName); - - FILE* ifile = fopen(inputFileName, "rb"); - FILE* ofile = fopen(outputFileName, "ab"); //'append binary' - - Byte tsBuffer[tsLength]; - for (uint32_t i = 0; i < result->nbClusters; i++) - { - // Get time-series in (binary) inputFile - fseek(ifile, 8 + result->medoids_ranks[i] * tsLength, SEEK_SET); - // Copy current serie onto ofile - - size_t lengthRead = fread(tsBuffer, 1, tsLength, ifile); - if (lengthRead != tsLength) - fprintf(stderr, "problem while copying binary series to new file.\n"); - fwrite(tsBuffer, 1, tsLength, ofile); - } - - fclose(ifile); - fclose(ofile); -} - -// fill a new unit of work suitable to be given to a slave -static Work_t* get_next_work(char* inputFileName, uint32_t nbSeries, uint32_t nbSeriesInChunk, - double idealNbSeriesInChunk, uint32_t jobsSentCount, uint32_t lastEndIndex, - uint32_t nbClusters, uint32_t clustOnMedoids, - int randomize, uint32_t p_for_dissims) -{ - Work_t* work = (Work_t*) malloc(sizeof(Work_t)); - - work->inputFileName = (char*) malloc(strlen(inputFileName) + 1); - strcpy(work->inputFileName, inputFileName); - - if (randomize) - work->nbSeries = nbSeriesInChunk; - else - { - double adjustedNbSeriesInNextChunk = - idealNbSeriesInChunk * (jobsSentCount + 1) - lastEndIndex; - // round to closest integer - work->nbSeries = (uint32_t)adjustedNbSeriesInNextChunk; - // stay below the upper bound (TODO: is this check required ?) - if (work->nbSeries > nbSeriesInChunk) - work->nbSeries = nbSeriesInChunk; - // TODO: what about this one ? (probably useless) - if (lastEndIndex + work->nbSeries > nbSeries) - work->nbSeries = nbSeries - lastEndIndex; - } - - //TODO: ranks on uint64_t if more than 4.3 billion series at the same place (unlikely...) - work->ranks = (uint32_t*) malloc(work->nbSeries * sizeof(uint32_t)); - for (uint32_t i = 0; i < work->nbSeries; i++) - work->ranks[i] = (randomize ? get_rand_int() % nbSeries : lastEndIndex + i); - - work->nbClusters = nbClusters; - work->clustOnMedoids = clustOnMedoids; - work->p_for_dissims = p_for_dissims; - - return work; -} - -// process all subtasks and save binary results into a new file -// NOTE: this file will be smaller than initial file (or DB...) -static void clusters_reduce(char* inputFileName, char* outputFileName, uint32_t ntasks, - uint32_t totalNbSeries, uint32_t nbSeriesInChunk, double idealNbSeriesInChunk, - uint32_t tsLength, uint32_t nbClusters, uint32_t clustOnMedoids, - int randomize, uint32_t p_for_dissims) -{ - FILE* ofile = fopen(outputFileName, "wb"); //'write binary' - // Leave a blank for series' count and tsLength - for (uint32_t i = 0; i < 8; i++) - fputc(0, ofile); - fclose(ofile); - - uint32_t jobsSentCount = 0; //used if randomize==FALSE - uint32_t lastEndIndex = 0; //used if randomize==FALSE - - uint32_t sentSeriesCount = 0; //used if randomize==TRUE - - // Count series sent to binary file on output - uint32_t newSeriesCount = 0; - - // Expected size of a Work message in bytes: - uint32_t work_message_length = get_packedWork_length(nbSeriesInChunk); - Byte packedWork[work_message_length]; - - // Expected size of a Result message in bytes: (uint32_t is on 4 bytes) - uint32_t result_message_length = 4 + 4 * nbClusters + 4 * nbClusters; - Byte packedResult[result_message_length]; - - // Seed the slaves; send one unit of work to each slave. - Work_t* work; - int* busy_slave = (int*) malloc(ntasks * sizeof(int)); - for (int rank = 1; rank < ntasks; rank++) - busy_slave[rank] = 0; - for (int rank = 1; rank < ntasks; rank++) - { - // Find the next item of work to do - work = get_next_work(inputFileName, totalNbSeries, nbSeriesInChunk, idealNbSeriesInChunk, - jobsSentCount, lastEndIndex, nbClusters, clustOnMedoids, - randomize, p_for_dissims); - - if (randomize) - sentSeriesCount += nbSeriesInChunk; - else - { - lastEndIndex = lastEndIndex + work->nbSeries; - jobsSentCount++; - } - - // Send it to current rank - pack_work(work, nbSeriesInChunk, packedWork); - free_work(work); - fprintf(stdout, "0 / Send work %s to rank=%i / %u\n",inputFileName, rank, - (randomize ? sentSeriesCount : lastEndIndex)); - MPI_Send(packedWork, work_message_length, MPI_BYTE, rank, WORKTAG, MPI_COMM_WORLD); - - busy_slave[rank] = 1; - - if ((randomize && sentSeriesCount >= 1.5*totalNbSeries) //TODO: 1.5 = heuristic, magic number... - || (!randomize && lastEndIndex >= totalNbSeries)) - { - // Nothing more to read - break; - } - } - - // Loop over getting new work requests until there is no more work to be done - Result_t* result; - MPI_Status status; - while (1) - { - // If no slave is active, job is over - int atLeastOneSlaveActive = 0; - for (int rank = 1; rank < ntasks; rank++) - { - if (busy_slave[rank]) - { - atLeastOneSlaveActive = 1; - break; - } - } - if (!atLeastOneSlaveActive) - break; - - // Receive results from a slave - MPI_Recv(packedResult, result_message_length, MPI_BYTE, MPI_ANY_SOURCE, - WORKTAG, MPI_COMM_WORLD, &status); - result = unpack_result(packedResult); - fprintf(stdout, "0 / Receive result from rank=%i on %s\n",status.MPI_SOURCE,inputFileName); - - // 'binarize' the result (only series' values) returned by the slave - binaryResult_to_file(result, inputFileName, outputFileName); - free_result(result); - newSeriesCount += nbClusters; - - if ((randomize && sentSeriesCount < totalNbSeries) - || (!randomize && lastEndIndex < totalNbSeries)) - { - // Get the next unit of work to be done - work = get_next_work(inputFileName, totalNbSeries, nbSeriesInChunk, - idealNbSeriesInChunk, jobsSentCount, lastEndIndex, nbClusters, - clustOnMedoids, randomize, p_for_dissims); - - if (randomize) - sentSeriesCount += nbSeriesInChunk; - else - { - lastEndIndex = lastEndIndex + work->nbSeries; - jobsSentCount++; - } - - // Send the slave a new work unit - pack_work(work, nbSeriesInChunk, packedWork); - free_work(work); - fprintf(stdout, "0 / Send work %s to rank=%i / %u\n",inputFileName, status.MPI_SOURCE, - (randomize ? sentSeriesCount : lastEndIndex)); - MPI_Send(packedWork, work_message_length, MPI_BYTE, - status.MPI_SOURCE, WORKTAG, MPI_COMM_WORLD); - } - - else - // No more work to do - busy_slave[status.MPI_SOURCE] = 0; - } - - // There's no more work to be done, so receive all results from the slaves. - for (int rank = 1; rank < ntasks; rank++) - { - if (busy_slave[rank]) - { - MPI_Recv(packedResult, result_message_length, MPI_BYTE, - rank, WORKTAG, MPI_COMM_WORLD, &status); - result = unpack_result(packedResult); - - // 'binarize' the result (only series' values) returned by the slave - binaryResult_to_file(result, inputFileName, outputFileName); - free_result(result); - newSeriesCount += nbClusters; - } - } - free(busy_slave); - - // Finalize output file: write total number of series inside it, and tsLength - ofile = fopen(outputFileName, "r+b"); //read and write, binary - fseek(ofile, 0, SEEK_SET); - Byte intBuffer[4]; - write_int(newSeriesCount, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - write_int(tsLength, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - fclose(ofile); -} - -// generate random temporary file names -static char* get_unique_name() -{ - size_t nbDigits = 7; //rather arbitrary - size_t stringLength = 5 + nbDigits; //5 for '.tmp/' - char* s = (char*) malloc(stringLength + 1); - s[0] = '.'; - s[1] = 't'; - s[2] = 'm'; - s[3] = 'p'; - s[4] = '/'; - for (int i=0; i<nbDigits; i++) - s[5+i] = '0' + get_rand_int() % 10; - s[stringLength] = 0; - return s; -} - -// code executed by master process -void master_run(char* mainInputFileName, uint32_t totalNbSeries, uint32_t nbSeriesInChunk, - double idealNbSeriesInChunk, uint32_t tsLength, uint32_t nbClusters, - int randomize, uint32_t p_for_dissims) -{ - // Basic sanity check: nbClusters must be clearly less than series count per chunk - if (10 * nbClusters >= nbSeriesInChunk) - { - fprintf(stdout, "WARNING: cluster size (%u) may be too high compared with chunk size (%u).\n", - nbClusters, nbSeriesInChunk); - } - - // Find out how many processes there are in the default communicator - int ntasks; - MPI_Comm_size(MPI_COMM_WORLD, &ntasks); - if (ntasks <= 1) - { - fprintf(stderr,"No slaves available (only master running).\n"); - return; - } - - // initializations - char* inputFileName = mainInputFileName; - char* outputFileName; - - uint32_t nbSeriesInFile = totalNbSeries; - - while (nbSeriesInFile > nbSeriesInChunk) - { - outputFileName = get_unique_name(); - uint32_t clustOnMedoids = (nbSeriesInFile < totalNbSeries ? 1 : 0); - clusters_reduce(inputFileName, outputFileName, ntasks, nbSeriesInFile, nbSeriesInChunk, - idealNbSeriesInChunk, tsLength, nbClusters, clustOnMedoids, - randomize, p_for_dissims); - - // read nbSeries in outputFile - nbSeriesInFile = get_nbSeries(outputFileName); - - // update file names - if (strcmp(mainInputFileName, inputFileName) != 0) - { - // No need to keep every intermediate binary - unlink(inputFileName); - free(inputFileName); - } - inputFileName = outputFileName; - } - - // read nbSeries in inputFileName (the last one) - // we know that there is at most 'nbSeriesInChunk' series in it - nbSeriesInFile = get_nbSeries(inputFileName); - - // Expected size of a Work message in bytes: - uint32_t work_message_length = get_packedWork_length(nbSeriesInChunk); - Byte packedWork[work_message_length]; - - // Expected size of a Result message in bytes: (uint32_t is on 4 bytes) - uint32_t result_message_length = get_packedResult_length(nbClusters); - Byte packedResult[result_message_length]; - - // Run a last task by some slave, and get final result - Work_t* work = get_next_work(inputFileName, nbSeriesInFile, nbSeriesInChunk, - (double)nbSeriesInFile, 0, 0, nbClusters, 1, 0, p_for_dissims); - - // Send the slave a new work unit - pack_work(work, nbSeriesInChunk, packedWork); - free_work(work); - int selectedSlaveRank = get_rand_int() % (ntasks - 1) + 1; - fprintf(stdout, "0 / Send final work %s to rank=%i / %u\n", - inputFileName, selectedSlaveRank, nbSeriesInFile); - MPI_Send(packedWork, work_message_length, MPI_BYTE, selectedSlaveRank, - WORKTAG, MPI_COMM_WORLD); - - MPI_Status status; - // Wait for him to finish - MPI_Recv(packedResult, result_message_length, MPI_BYTE, selectedSlaveRank, - WORKTAG, MPI_COMM_WORLD, &status); - Result_t* finalResult = unpack_result(packedResult); - fprintf(stdout, "0 / Receive final result from rank=%i on %s\n",status.MPI_SOURCE,inputFileName); - - //Tell all the slaves to exit by sending an empty message with the DIETAG. - for (int rank = 1; rank < ntasks; ++rank) - MPI_Send(0, 0, MPI_BYTE, rank, DIETAG, MPI_COMM_WORLD); - - const char finalBinaryName[] = "ppamFinalSeries.bin"; - result_to_XML(finalResult, inputFileName, finalBinaryName, p_for_dissims); - free_result(finalResult); - - // free memory - if (strcmp(mainInputFileName, inputFileName)) - { - // Keep last input binary, but rename it - rename(inputFileName, finalBinaryName); - free(inputFileName); - } - else - { - // just symlink mainInputFileName - if (!access(finalBinaryName, F_OK)) - unlink(finalBinaryName); - if (symlink(mainInputFileName, finalBinaryName)) - fprintf(stderr,"Cannot create symlink to initial binary file.\n"); - } -} diff --git a/old_C_code/stage1/src/MPI_Main/master.h b/old_C_code/stage1/src/MPI_Main/master.h deleted file mode 100644 index 397ea59..0000000 --- a/old_C_code/stage1/src/MPI_Main/master.h +++ /dev/null @@ -1,11 +0,0 @@ -#ifndef PPAM_MASTER_H -#define PPAM_MASTER_H - -#include <stdint.h> - -// code executed by master process -void master_run(char* fileName, uint32_t totalNbSeries, uint32_t nbSeriesInChunk, - double idealNbSeriesInChunk, uint32_t tsLength, uint32_t nbClusters, - int randomize, uint32_t p_for_dissims); - -#endif diff --git a/old_C_code/stage1/src/MPI_Main/slave.c b/old_C_code/stage1/src/MPI_Main/slave.c deleted file mode 100644 index d7d0afe..0000000 --- a/old_C_code/stage1/src/MPI_Main/slave.c +++ /dev/null @@ -1,111 +0,0 @@ -#include "Util/types.h" -#include "Util/utils.h" -#include "TimeSeries/deserialize.h" -#include "Algorithm/compute_coefficients.h" -#include "Algorithm/get_dissimilarities.h" -#include "Algorithm/pam.h" -#include <mpi.h> -#include <stdio.h> -#include <math.h> -#include <stdlib.h> -#include <unistd.h> -#include "MPI_Communication/unpack.h" -#include "MPI_Communication/pack.h" - -// main job done by a slave: -static Result_t* do_work(Work_t* work) -{ - // *** Step 1 *** - // Obtain reduced coordinates matrix from time-series. - - uint32_t nbSeries = work->nbSeries; - uint32_t tsLength = get_tsLength(work->inputFileName); - uint32_t nbValues = (tsLength - 4) / 4; - - // nbReducedCoordinates = smallest power of 2 which is above nbValues - uint32_t nbReducedCoordinates = (uint32_t)ceil(log2(nbValues)); - float* reducedCoordinates = (float*) malloc(nbSeries * nbReducedCoordinates * sizeof(float)); - - // call preprocessing with the rows of raw power values matrix. - // Keep the IDs in memory for further processing. - uint32_t* IDs = (uint32_t*)malloc(nbSeries*sizeof(uint32_t)); - for (uint32_t i = 0; i < nbSeries; i+=CURVES_PER_REQUEST) - { - uint32_t nbCurvesRetrieved = CURVES_PER_REQUEST; - if (i + nbCurvesRetrieved > nbSeries) - nbCurvesRetrieved -= (i + nbCurvesRetrieved - nbSeries); - PowerCurve* powerCurves = - deserialize(work->inputFileName, NULL, work->ranks + i, nbCurvesRetrieved); - compute_coefficients(powerCurves, nbCurvesRetrieved, nbValues, - reducedCoordinates, i, nbReducedCoordinates); - for (uint32_t ii=i; ii<i+nbCurvesRetrieved; ii++) - { - IDs[ii] = powerCurves[ii-i].ID; - free(powerCurves[ii-i].values); - } - free(powerCurves); - } - - // *** Step 2 *** - // Run PAM algorithm on the dissimilarity matrix computed from 'reducedCoordinates'. - - float* dissimilarities = get_dissimilarities_intra( - reducedCoordinates, nbSeries, nbReducedCoordinates, work->p_for_dissims); - free(reducedCoordinates); - - uint32_t nbClusters = work->nbClusters; - Result_t* result = (Result_t*) malloc(sizeof(Result_t)); - result->medoids_ID = (uint32_t*) malloc(nbClusters * sizeof(uint32_t)); - result->medoids_ranks = (uint32_t*) malloc(nbClusters * sizeof(uint32_t)); - result->nbClusters = nbClusters; - - // Run PAM algorithm to fill result->medoids_ranks - pam(dissimilarities, nbSeries, nbClusters, work->clustOnMedoids, NSTART, MAXITER, result); - free(dissimilarities); - - // Deduce medoids_IDs from indices - for (uint32_t i = 0; i < nbClusters; i++) - result->medoids_ID[i] = IDs[result->medoids_ranks[i]]; - free(IDs); - - // return medoids IDs, indices and items labels (to be post-processed) - return result; -} - -// code executed by slave process -void slave_run(int myrank, uint32_t nbSeriesInChunk, uint32_t nbClusters) -{ - Work_t* work; - Result_t* result; - MPI_Status status; - - // Expected size of a Work message in bytes: - uint32_t work_message_length = get_packedWork_length(nbSeriesInChunk); - Byte packedWork[work_message_length]; - - // Expected size of a Result message in bytes: (uint32_t is on 4 bytes) - uint32_t result_message_length = get_packedResult_length(nbClusters); - Byte packedResult[result_message_length]; - - while (1) - { - // Receive a message from the master - MPI_Recv(packedWork, work_message_length, MPI_BYTE, 0, - MPI_ANY_TAG, MPI_COMM_WORLD, &status); - - // Check the tag of the received message. - if (status.MPI_TAG == DIETAG) - return; - - // Do the work - work = unpack_work(packedWork, nbSeriesInChunk); - fprintf(stdout, "%u / Slave pid=%u work on %s\n",myrank,getpid(),work->inputFileName); - result = do_work(work); - free_work(work); - - // Send the result back - pack_result(result, packedResult); - free_result(result); - MPI_Send(packedResult, result_message_length, MPI_BYTE, 0, WORKTAG, MPI_COMM_WORLD); - } -} diff --git a/old_C_code/stage1/src/MPI_Main/slave.h b/old_C_code/stage1/src/MPI_Main/slave.h deleted file mode 100644 index b5ef443..0000000 --- a/old_C_code/stage1/src/MPI_Main/slave.h +++ /dev/null @@ -1,9 +0,0 @@ -#ifndef PPAM_SLAVE_H -#define PPAM_SLAVE_H - -#include <stdint.h> - -// code executed by slave process -void slave_run(int myrank, uint32_t nbSeriesInChunk, uint32_t nbClusters); - -#endif diff --git a/old_C_code/stage1/src/TimeSeries/deserialize.c b/old_C_code/stage1/src/TimeSeries/deserialize.c deleted file mode 100644 index e142fe3..0000000 --- a/old_C_code/stage1/src/TimeSeries/deserialize.c +++ /dev/null @@ -1,81 +0,0 @@ -#include "TimeSeries/deserialize.h" -#include <stdio.h> -#include "Util/utils.h" - -// Deserialize a portion of a binary file into an array of PowerCurve, or a file -// WARNING: NO start bytes, NO date times (all series have the same length, at same times). -PowerCurve* deserialize(const char* ifileName, const char* ofileName, - uint32_t* ranks, uint32_t nbRanks) -{ - // Read tsLength at the beginning of the file - uint32_t tsLength = get_tsLength(ifileName); - - uint32_t valuesPerSerie = (tsLength - 4) / 4; //remove 4 bytes of ID - - FILE* ifile = fopen(ifileName, "rb"); - FILE* ofile = NULL; - if (ofileName) - ofile = fopen(ofileName, "w"); - - if (!ranks || nbRanks <= 0) - { - nbRanks = get_nbSeries(ifileName); - ranks = NULL; - } - - PowerCurve* powerCurves = NULL; - if (!ofile) - powerCurves = (PowerCurve*) malloc(nbRanks * sizeof(PowerCurve)); - - for (uint32_t i = 0; i < nbRanks; i++) - { - // position to the beginning of current (binarized) time-series - // NOTE: shift by 8 bytes, because data size and series length are written first - fseek(ifile, 8 + (ranks ? ranks[i] : i) * tsLength, SEEK_SET); - - PowerCurve* powerCurve; - if (!ofile) - { - powerCurve = powerCurves + i; - powerCurve->values = (float*) malloc(valuesPerSerie * sizeof(float)); - } - - // translate 4-bytes binary integer into integer ID - void* binaryID = malloc(4); - size_t lengthRead = fread(binaryID, 4, 1, ifile); - if (lengthRead != 1) - fprintf(stderr,"Warning: deserializing truncated binary file.\n"); - uint32_t ID = bInt_to_uint((Byte*) binaryID); - free(binaryID); - if (ofile) - fprintf(ofile, "%u,", ID); - else - powerCurve->ID = ID; - - // translate 4-bytes binary integers into float - Byte* binarySerie = (Byte*) malloc(4 * valuesPerSerie); - lengthRead = fread(binarySerie, 1, 4*valuesPerSerie, ifile); - //TODO: assert that lengthRead == 4*valuesPerSerie (...) - for (uint32_t i = 0; i < valuesPerSerie; i++) - { - float power = bReal_to_float(binarySerie + 4 * i); - if (ofile) - { - fprintf(ofile, "%g", power); - if (i < valuesPerSerie-1) - fprintf(ofile, ","); - } - else - powerCurve->values[i] = power; - } - free(binarySerie); - if (ofile) - fprintf(ofile, "\n"); - } - - fclose(ifile); - if (ofile) - fclose(ofile); - - return powerCurves; -} diff --git a/old_C_code/stage1/src/TimeSeries/deserialize.h b/old_C_code/stage1/src/TimeSeries/deserialize.h deleted file mode 100644 index aa6a3d6..0000000 --- a/old_C_code/stage1/src/TimeSeries/deserialize.h +++ /dev/null @@ -1,12 +0,0 @@ -#ifndef PPAM_DESERIALIZE_H -#define PPAM_DESERIALIZE_H - -#include <stdint.h> -#include <stdlib.h> -#include "Util/types.h" - -// deserialize a portion of a binary file into an array of PowerCurve -PowerCurve* deserialize(const char* ifileName, const char* ofileName, - uint32_t* ranks, uint32_t nbRanks); - -#endif diff --git a/old_C_code/stage1/src/TimeSeries/serialize.c b/old_C_code/stage1/src/TimeSeries/serialize.c deleted file mode 100644 index ba52c87..0000000 --- a/old_C_code/stage1/src/TimeSeries/serialize.c +++ /dev/null @@ -1,284 +0,0 @@ -#include "TimeSeries/serialize.h" -#define __STDC_FORMAT_MACROS -#include <inttypes.h> -#include <math.h> -#include "Util/types.h" -#include "Util/utils.h" -#include <cgds/Vector.h> -#include <string.h> - -// parse a line into two integers (ID, raw power) -static void scan_line(FILE* ifile, int posID, uint32_t* ID, int posPower, float* rawPower) -{ - char nextChar; - int position = 1; - while (1) - { - if (position == posID) - { - int64_t ID_on64bits; - nextChar = readInt(ifile, &ID_on64bits); - *ID = (uint32_t)ID_on64bits; - } - else if (position == posPower) - { - float power; - nextChar = readReal(ifile, &power); - *rawPower = (float) power; - } - else - //erase the comma (and skip field then) - nextChar = fgetc(ifile); - - //continue until next comma (or line end or file end) - while (!feof(ifile) && nextChar != '\n' && nextChar != '\r' && nextChar != ',') - nextChar = fgetc(ifile); - position++; - - if (feof(ifile) || nextChar == '\n' || nextChar == '\r') - { - // skip all potential line feeds - while (!feof(ifile) && nextChar == '\n' || nextChar == '\r') - nextChar = fgetc(ifile); - if (!feof(ifile)) - ungetc(nextChar, ifile); - break; - } - } -} - -//main job: parse a text file into a binary compressed version -//TODO [long term]: adapt to distributed files/DB, maybe to distributed binary chunks -void serialize_byCols(const char* ifileName, const char* ofileName, uint32_t nbItems) -{ - // use the header to know positions of ID and rawPower - FILE* ifile = fopen(ifileName, "r"); - uint32_t headerShift = 0; - char curChar; - Vector* header = vector_new(char); - do - { - curChar = fgetc(ifile); - headerShift++; - if (curChar == '\n' || curChar == '\r') - { - //flush all potential other line feeds - while (curChar == '\n' || curChar == '\r') - curChar = fgetc(ifile); - ungetc(curChar, ifile); - break; - } - vector_push(header, curChar); - } - while (1); - char* headerString = (char*)malloc((vector_size(header) + 1)*sizeof(char)); - VectorIterator* it = vector_get_iterator(header); - int index = 0; - while (vectorI_has_data(it)) - { - vectorI_get(it, headerString[index]); - vectorI_move_next(it); - index++; - } - vectorI_destroy(it); - headerString[index] = 0; - vector_destroy(header); - int position = 1, posID = 0, posPower = 0; - char* columnDescriptor = strtok(headerString, ","); - while (columnDescriptor != NULL) - { - if (!strcmp(columnDescriptor,"FK_CCU_ID") || !strcmp(columnDescriptor,"fk_ccu_id")) - posID = position; - else if (!strcmp(columnDescriptor,"CPP_PUISSANCE_BRUTE")) - posPower = position; - position++; - columnDescriptor = strtok(NULL, ","); - } - free(headerString); - - //estimate tsLength with a scan of the 3 first series - uint32_t ID=0, lastID=0, refTsLength=0; - float rawPower = 0.0; - scan_line(ifile, posID, &ID, posPower, &rawPower); - //'sl' = sample lengths (short because a lot of comparisons then) - uint32_t* sl = (uint32_t*) calloc(3, sizeof(uint32_t)); - for (int i=0; i<3; i++) - { - lastID = ID; - while (ID == lastID) - { - sl[i]++; - scan_line(ifile, posID, &ID, posPower, &rawPower); - } - } - if (sl[1] <= sl[0] <= sl[2] || sl[2] <= sl[0] <= sl[1]) - refTsLength = sl[0]; - else if (sl[0] <= sl[1] <= sl[2] || sl[2] <= sl[1] <= sl[0]) - refTsLength = sl[1]; - else - refTsLength = sl[2]; - free(sl); - //go back at the beginning of the first series (ready to read '\n'...) - fseek(ifile, headerShift-1, SEEK_SET); - - // output file to write time-series sequentially, binary format. - // Format: for each series, ID is stored on 4 bytes (unsigned integer32). Then, - // (<rawPower>)+ follow, with rawPower stored as a float - FILE* ofile = fopen(ofileName, "wb"); - - // leave space to write the number of series (32bits), and their length in bytes (32bits) - for (int i = 0; i < 8; i++) - fputc(0, ofile); - - // process one client (ID in first column) at a time - uint64_t processedLines = 0; //execution trace - uint32_t seriesCount=0, skippedSeriesCount=0, tsLength=0; - uint32_t mismatchLengthCount=0; - Byte tsBuffer[4+4*refTsLength]; - lastID = 0; - while (!feof(ifile)) - { - // next element to read always start with a digit - do - curChar = fgetc(ifile); - while (!feof(ifile) && (curChar < '0' || curChar > '9')); - if (feof(ifile)) - break; - ungetc(curChar, ifile); - - // read line - scan_line(ifile, posID, &ID, posPower, &rawPower); - if (ID != lastID) - { - //just starting a new time-series: must process the last one (if there is a last one !) - if (lastID > 0) - { - if (tsLength == refTsLength) - { - seriesCount++; - fwrite(tsBuffer, 4+4*tsLength, 1, ofile); - if (nbItems > 0 && seriesCount >= nbItems) - break; - } - //if something wrong happened, skip series - else - { - skippedSeriesCount++; - if (tsLength != refTsLength) - mismatchLengthCount++; - } - } - - // ID for the new series is printed only once: - write_int(ID, tsBuffer); - // reinitialize flags - tsLength = 0; - lastID = ID; - } - - //We cannot write more than refTsLength bytes - if (tsLength < refTsLength) - write_real(rawPower, tsBuffer + 4+4*tsLength); - tsLength++; - - if ((++processedLines) % 1000000 == 0) - fprintf(stdout,"Processed %"PRIu64" lines\n", processedLines); - } - - if (tsLength == refTsLength && (nbItems <= 0 || seriesCount < nbItems)) - { - // flush last time-series if all conditions are met - fwrite(tsBuffer, 4+4*tsLength, 1, ofile); - seriesCount++; - } - else if (nbItems <= 0 || seriesCount < nbItems) - { - if (tsLength != refTsLength) - mismatchLengthCount++; - } - - // write lines count and size of a time-series in bytes - Byte intBuffer[4]; - fseek(ofile, 0, SEEK_SET); - write_int(seriesCount, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - // re-express tsLength in bytes (not forgetting the ID)) - write_int(4 + 4 * refTsLength, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - - // finally print some statistics - if (seriesCount < nbItems) - fprintf(stdout,"Warning: only %u series retrieved.\n",seriesCount); - fprintf(stdout,"%u mismatch series lengths.\n",mismatchLengthCount); - - fclose(ifile); - fclose(ofile); -} - -//serialize from usual 'by-row' data -void serialize_byRows(const char* ifileName, const char* ofileName, uint32_t nbItems) -{ - FILE* ifile = fopen(ifileName, "r"); - // first scan to know dimensions - uint32_t nbValues = 0; //ID (or, more useful, real class number) comes first - char curChar = ' '; - while (curChar != '\n' && curChar != '\r') - { - curChar = fgetc(ifile); - if (curChar == ',') - { - nbValues++; - //skip potential consecutive commas (could be hard to spot) - //TODO(...): should be 'NA' in R, thus an error (we don't handle NAs)... - while (curChar == ',') - curChar = fgetc(ifile); - ungetc(curChar, ifile); - } - } - while (curChar == '\n' || curChar == '\r') - curChar = fgetc(ifile); - ungetc(curChar, ifile); - uint32_t nbSeries = 1; //we already read 1st line - while (!feof(ifile)) - { - if ((curChar = fgetc(ifile)) == '\n') - nbSeries++; - } - fseek(ifile, 0, SEEK_SET); - - //write meta info first - uint32_t tsLength = 4*nbValues+4; - FILE* ofile = fopen(ofileName, "wb"); - Byte intBuffer[4]; - write_int(nbSeries, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - write_int(tsLength, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - float rawPower; - int64_t ID; - - for (uint32_t i=0; i<nbSeries; i++) - { - //skip potential line feeds before next line - while (curChar == '\n' || curChar == '\r') - curChar = fgetc(ifile); - ungetc(curChar, ifile); - curChar = readInt(ifile, &ID); - write_int((uint32_t)ID, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - while (curChar == ',') - curChar = fgetc(ifile); - ungetc(curChar, ifile); - for (uint32_t j=0; j<nbValues; j++) - { - curChar = readReal(ifile, &rawPower); - write_real(rawPower, intBuffer); - fwrite(intBuffer, 1, 4, ofile); - while (curChar == ',') - curChar = fgetc(ifile); - ungetc(curChar, ifile); - } - } - fclose(ifile); - fclose(ofile); -} diff --git a/old_C_code/stage1/src/TimeSeries/serialize.h b/old_C_code/stage1/src/TimeSeries/serialize.h deleted file mode 100644 index af89cae..0000000 --- a/old_C_code/stage1/src/TimeSeries/serialize.h +++ /dev/null @@ -1,14 +0,0 @@ -#ifndef PPAM_SERIALIZE_H -#define PPAM_SERIALIZE_H - -#include <stdlib.h> -#include <stdio.h> -#include <stdint.h> - -//main job: parse a text file into a binary compressed version -void serialize_byCols(const char* ifileName, const char* ofileName, uint32_t nbItems); - -//serialize from usual 'by-row' data -void serialize_byRows(const char* ifileName, const char* ofileName, uint32_t nbItems); - -#endif diff --git a/old_C_code/stage1/src/Util/rng.c b/old_C_code/stage1/src/Util/rng.c deleted file mode 100644 index 2933174..0000000 --- a/old_C_code/stage1/src/Util/rng.c +++ /dev/null @@ -1,135 +0,0 @@ -// ISAAC PRNG -// Initial code by Bob Jenkins, March 1996 - modified in 2008 -// Reference: http://burtleburtle.net/bob/rand/isaacafa.html -// Further slightly modified by Benjamin Auder, 2013 - -#include "Util/rng.h" -#include <stdlib.h> -#include <time.h> - -// Internal state -static uint32_t mm[256]; -static uint32_t aa, bb, cc; - -// initialize the (pseudo-)random generator -void init_rng(int flag) -{ - // 'bootstrap' isaac with basic bad PRNG - srand(time(NULL)); - - aa = rand() & 0xffffffff; - bb = rand() & 0xffffffff; - cc = rand() & 0xffffffff; - - uint32_t a, b, c, d, e, f, g, h, i; - // The golden ratio: - a = b = c = d = e = f = g = h = 0x9e3779b9; - // Scramble it - for (i = 0; i < 4; ++i) - mix(a, b, c, d, e, f, g, h); - - // Fill in mm[] with messy stuff (the seed) - uint32_t seedArray[256]; - for (i = 0; i < 256; ++i) mm[i] = seedArray[i] = rand() & 0xffffffff; - for (i = 0; i < 256; i += 8) - { - if (flag) - { - // Use all the information in the seed - a += seedArray[i ]; - b += seedArray[i + 1]; - c += seedArray[i + 2]; - d += seedArray[i + 3]; - e += seedArray[i + 4]; - f += seedArray[i + 5]; - g += seedArray[i + 6]; - h += seedArray[i + 7]; - } - mix(a, b, c, d, e, f, g, h); - mm[i ] = a; - mm[i + 1] = b; - mm[i + 2] = c; - mm[i + 3] = d; - mm[i + 4] = e; - mm[i + 5] = f; - mm[i + 6] = g; - mm[i + 7] = h; - } - - if (flag) - { - // Do a second pass to make all of the seed affect all of mm - for (i = 0; i < 256; i += 8) - { - a += mm[i ]; - b += mm[i + 1]; - c += mm[i + 2]; - d += mm[i + 3]; - e += mm[i + 4]; - f += mm[i + 5]; - g += mm[i + 6]; - h += mm[i + 7]; - mix(a, b, c, d, e, f, g, h); - mm[i ] = a; - mm[i + 1] = b; - mm[i + 2] = c; - mm[i + 3] = d; - mm[i + 4] = e; - mm[i + 5] = f; - mm[i + 6] = g; - mm[i + 7] = h; - } - } -} - -// return a (pseudo-)random integer -uint32_t get_rand_int() -{ - // TODO: register variables ? (x,y,i) - uint32_t x, y; - static uint32_t i = 0; - - if (i == 0) - { - cc = cc + 1; // cc just gets incremented once per 256 results - bb = bb + cc; // then combined with bb - } - - x = mm[i]; - switch (i % 4) - { - case 0: - aa = aa^(aa << 13); - break; - case 1: - aa = aa^(aa >> 6); - break; - case 2: - aa = aa^(aa << 2); - break; - case 3: - aa = aa^(aa >> 16); - break; - } - - // NOTE: bits 2..9 are chosen from x but 10..17 are chosen - // from y. The only important thing here is that 2..9 and 10..17 - // don't overlap. 2..9 and 10..17 were then chosen for speed in - // the optimized version (rand.c) */ - // See http://burtleburtle.net/bob/rand/isaac.html - // for further explanations and analysis. - - aa = mm[(i + 128) % 256] + aa; - mm[i] = y = mm[(x >> 2) % 256] + aa + bb; - bb = mm[(y >> 10) % 256] + x; - - i = (i + 1) % 256; - - return bb; -} - -// return a (pseudo-)random real number in [0,1] -float get_rand_real() -{ - return (float) (INV_RANDMAX * (double) get_rand_int()); -} diff --git a/old_C_code/stage1/src/Util/rng.h b/old_C_code/stage1/src/Util/rng.h deleted file mode 100644 index 371ff27..0000000 --- a/old_C_code/stage1/src/Util/rng.h +++ /dev/null @@ -1,33 +0,0 @@ -#ifndef PPAM_RNG_H -#define PPAM_RNG_H - -#include <stdint.h> -#include "Util/types.h" - -#define INV_RANDMAX 2.3283064370807974E-10 // 1/0xffffffff - -// If (flag!=0), then use a random array to initialize mm[] -#define mix(a,b,c,d,e,f,g,h) \ -{ \ - a^=b<<11; d+=a; b+=c; \ - b^=c>>2; e+=b; c+=d; \ - c^=d<<8; f+=c; d+=e; \ - d^=e>>16; g+=d; e+=f; \ - e^=f<<10; h+=e; f+=g; \ - f^=g>>4; a+=f; g+=h; \ - g^=h<<8; b+=g; h+=a; \ - h^=a>>9; c+=h; a+=b; \ -} - -// initialize the (pseudo-)random generator -// NOTE: flag has only two states, active or not. -// flag != 0 --> "maximal randomness" -void init_rng(int flag); - -// return a (pseudo-)random integer -uint32_t get_rand_int(); - -// return a (pseudo-)random real number in [0,1] -float get_rand_real(); - -#endif diff --git a/old_C_code/stage1/src/Util/types.h b/old_C_code/stage1/src/Util/types.h deleted file mode 100644 index 6b6da6f..0000000 --- a/old_C_code/stage1/src/Util/types.h +++ /dev/null @@ -1,44 +0,0 @@ -#ifndef PPAM_TYPES_H -#define PPAM_TYPES_H - -#include <stdint.h> - -// types of work given to a slave -#define WORKTAG 1 -#define DIETAG 2 - -// 256 characters for file name should be enough -#define NCHAR_FNAME 256 - -typedef unsigned char Byte; - -// Type to describe a job to be done in a node -//TODO: merge with packed version to avoid extra copy by MPI -typedef struct Work_t { - // "structural" parameters: - char* inputFileName; - uint32_t nbSeries; - uint32_t* ranks; - // clustering parameters [to be completed]: - uint32_t nbClusters; - uint32_t clustOnMedoids; //a boolean, but 1 byte storage would be inefficient - uint32_t p_for_dissims; -} Work_t; - -// Type returned by a worker (clusters labels and medoids) -//TODO: merge with packed version to avoid extra copy by MPI -typedef struct Result_t { - // parameters describing sizes - uint32_t nbClusters; - // informative parameters: - uint32_t* medoids_ID; - uint32_t* medoids_ranks; -} Result_t; - -// data structure to store a customer ID + [time-]serie -typedef struct PowerCurve { - uint32_t ID; - float* values; -} PowerCurve; - -#endif diff --git a/old_C_code/stage1/src/Util/utils.c b/old_C_code/stage1/src/Util/utils.c deleted file mode 100644 index 91ea075..0000000 --- a/old_C_code/stage1/src/Util/utils.c +++ /dev/null @@ -1,138 +0,0 @@ -#include "Util/utils.h" -#include <math.h> -#include <cgds/Vector.h> - -void free_work(Work_t* work) -{ - free(work->inputFileName); - free(work->ranks); - free(work); -} - -void free_result(Result_t* result) -{ - free(result->medoids_ID); - free(result->medoids_ranks); - free(result); -} - -char readInt(FILE* stream, int64_t* integer) -{ - *integer = 0; - char curChar = fgetc(stream); - int sign = (curChar == '-' ? -1 : 1); - while (curChar < '0' || curChar > '9') - curChar = fgetc(stream); - ungetc(curChar, stream); - while ((curChar = fgetc(stream)) >= '0' && curChar <= '9') - *integer = 10 * (*integer) + (int64_t) (curChar - '0'); - (*integer) *= sign; - return curChar; -} - -char readReal(FILE* stream, float* real) -{ - int64_t integerPart; - char nextChar = readInt(stream, &integerPart); - int64_t fractionalPart = 0; - int countZeros = 0; - if (nextChar == '.') - { - //need to count zeros - while ((nextChar = fgetc(stream)) == '0') - countZeros++; - if (nextChar >= '0' && nextChar <= '9') - { - ungetc(nextChar, stream); - nextChar = readInt(stream, &fractionalPart); - } - } - int64_t exponent = 0; - if (nextChar == 'e' || nextChar == 'E') - nextChar = readInt(stream, &exponent); - int64_t divisorForFractional = pow(10, floor(log10(fractionalPart > 0 ? fractionalPart : 1))+1); - *real = ( (float)integerPart - + (integerPart < 0 ? -1 : 1) * (float)fractionalPart/(divisorForFractional*pow(10,countZeros)) ) - * pow(10,exponent); - return nextChar; -} - -// convert n-bytes binary integers to uint32_t -uint32_t bInt_to_uint(Byte* pInteger) -{ - uint32_t res; - memcpy(&res, pInteger, 4); - return res; -} - -// serialize integers -void write_int(uint32_t x, Byte* buffer) -{ - union { - uint32_t i; - char bytes[4]; - } u; - u.i = x; - for (size_t i = 0; i < 4; i++) - buffer[i] = u.bytes[i]; -} - -//WARNING: assuming float is 32bits... -// convert 4-bytes binary float to float -float bReal_to_float(Byte* pFloat) -{ - float res; - memcpy(&res, pFloat, 4); - return res; -} - -//WARNING: assuming float is 32bits... -// serialize double with a NON-portable bytes order -void write_real(float x, Byte* buffer) -{ - union { - float d; - char bytes[4]; - } u; - u.d = x; - for (size_t i = 0; i < 4; i++) - buffer[i] = u.bytes[i]; -} - -// Expected size of a Work message in bytes: -uint32_t get_packedWork_length(uint32_t nbSeriesInChunk) -{ - return NCHAR_FNAME + 4 + 4*nbSeriesInChunk + 4 + 4 + 4; -} - -// Expected size of a Result message in bytes: (uint32_t is on 4 bytes) -uint32_t get_packedResult_length(uint32_t nbClusters) -{ - return 4 + 4 * nbClusters + 4 * nbClusters; -} - -// get metadata: nbSeries -uint32_t get_nbSeries(const char* ifileName) -{ - FILE* ifile = fopen(ifileName, "rb"); - fseek(ifile, 0, SEEK_SET); - Byte binaryInt[4]; - size_t lengthRead = fread(binaryInt, 4, 1, ifile); - if (lengthRead != 1) - fprintf(stderr,"Warning: getting nbSeries from truncated binary file.\n"); - fclose(ifile); - return bInt_to_uint(binaryInt); -} - -// get metadata: tsLength -uint32_t get_tsLength(const char* ifileName) -{ - FILE* ifile = fopen(ifileName, "rb"); - fseek(ifile, 4, SEEK_SET); - Byte binaryInt[4]; - size_t lengthRead = fread(binaryInt, 4, 1, ifile); - if (lengthRead != 1) - fprintf(stderr,"Warning: getting tsLength from truncated binary file.\n"); - fclose(ifile); - return bInt_to_uint(binaryInt); -} diff --git a/old_C_code/stage1/src/Util/utils.h b/old_C_code/stage1/src/Util/utils.h deleted file mode 100644 index 3cb5cd1..0000000 --- a/old_C_code/stage1/src/Util/utils.h +++ /dev/null @@ -1,40 +0,0 @@ -#ifndef PPAM_UTIL_UTILS_H -#define PPAM_UTIL_UTILS_H - -#include "Util/types.h" -#include <stdlib.h> -#include <stdio.h> - -#define CURVES_PER_REQUEST 50 - -void free_work(Work_t* work); - -void free_result(Result_t* result); - -char readInt(FILE* stream, int64_t* integer); - -char readReal(FILE* stream, float* real); - -// convert n-bytes binary integers to uint32_t -uint32_t bInt_to_uint(Byte* pInteger); - -// serialize integers with a portable bytes order -void write_int(uint32_t integer, Byte* buffer); - -float bReal_to_float(Byte* pFloat); - -void write_real(float x, Byte* buffer); - -// Expected size of a Work message in bytes: -uint32_t get_packedWork_length(uint32_t nbSeriesInChunk); - -// Expected size of a Result message in bytes: (uint32_t is on 4 bytes) -uint32_t get_packedResult_length(uint32_t nbClusters); - -// get metadata: nbSeries -uint32_t get_nbSeries(const char* ifileName); - -// get metadata: tsLength -uint32_t get_tsLength(const char* ifileName); - -#endif diff --git a/old_C_code/stage1/src/main.c b/old_C_code/stage1/src/main.c deleted file mode 100644 index d1f7965..0000000 --- a/old_C_code/stage1/src/main.c +++ /dev/null @@ -1,275 +0,0 @@ -#include "MPI_Main/master.h" -#include "MPI_Main/slave.h" -#include "Util/utils.h" -#include "Util/rng.h" -#include <sys/stat.h> -#include <mpi.h> -#include <math.h> -#include <stdio.h> -#include "TimeSeries/serialize.h" -#include "TimeSeries/deserialize.h" -#include "Classification/getClass.h" -#include <string.h> -#include <cgds/Vector.h> -#include <libxml/xmlreader.h> - -// serialize text file argv[1] into a binary file argv[2] -int serialize_main(int argc, char** argv) -{ - const char* ifileName = argv[1]; - const char* ofileName = argv[2]; - int byCols = atoi(argv[3]); - uint32_t nbItems = atoi(argv[4]); //==0 for "all series" - - if (byCols) - serialize_byCols(ifileName, ofileName, nbItems); - else - serialize_byRows(ifileName, ofileName, nbItems); - return 0; -} - -// deserialize binary file argv[1] into text file argv[2] -int deserialize_main(int argc, char** argv) -{ - const char* ifileName = argv[1]; - const char* ofileName = argv[2]; - Vector* vranks = vector_new(uint32_t); - //each token is at most two ints (a-b = from a to b included) - char* token = strtok(argv[3], ","); - int retrieveAll = 0; - uint32_t* ranks = NULL; - while (token) - { - //scan token to find middle position of '-' (if any) - int minusPos = -1; - int tokenLength = strlen(token); - //loop starts at index 1 because -N is allowed (and means 'everything') - for (int i=1; i<tokenLength; i++) - { - if (token[i] == '-') - { - minusPos = i; - break; - } - } - if (minusPos < 0) - { - int64_t rank = (int64_t)atoi(token); - if (rank <= 0) - { - retrieveAll = 1; - break; - } - vector_push(vranks, (uint32_t)rank); - } - else - { - token[minusPos] = 0; - int int1 = atoi(token); - int int2 = atoi(token+minusPos+1); - for (uint32_t i=int1; i<=int2; i++) - vector_push(vranks, i); - } - token = strtok(NULL, ","); - } - uint32_t nbRanks = retrieveAll - ? 0 - : vector_size(vranks); - if (!retrieveAll) - { - ranks = (uint32_t*) malloc(nbRanks*sizeof(uint32_t)); - for (uint32_t i=0; i<nbRanks; i++) - { - vector_get(vranks, i, ranks[i]); - ranks[i]--; //re-express on 0...{n-1} - } - } - vector_destroy(vranks); - - deserialize(ifileName, ofileName, ranks, nbRanks); - return 0; -} - -//main clustering task (master or slave) -int cluster_main(int argc, char **argv) -{ - MPI_Init(&argc, &argv); - - char* ifileName = argv[1]; //could be "../data/test.bin" - uint32_t nbSeriesInChunk = atoi(argv[2]); //could be 3000 - uint32_t nbClusters = atoi(argv[3]); //could be 15 - int randomize = atoi(argv[4]); //boolean - uint32_t p_for_dissims = atoi(argv[5]); //1 for L1, 2 for L2, ...etc - - // Get totalNbSeries and tsLength - uint32_t totalNbSeries = get_nbSeries(ifileName); - uint32_t tsLength = get_tsLength(ifileName); - - // Basic sanity checks - if (nbClusters <= 0 || nbSeriesInChunk <= 1) - { - MPI_Finalize(); - return 0; - } - if (nbSeriesInChunk > totalNbSeries) - nbSeriesInChunk = totalNbSeries; - if (nbClusters > nbSeriesInChunk) - nbClusters = nbSeriesInChunk; - - double idealNbSeriesInChunk = 0.0; //unused if randomize == TRUE - if (!randomize) - { - // Adjust nbSeriesInChunk to avoid small remainders. - // Each node should have at least nbSeriesInChunk (as given to the function). - - // ==> We seek for the largest N such that (double)totalNbSeries / N >= nbSeriesInChunk - uint32_t N = totalNbSeries / nbSeriesInChunk + 1; - while ((double)totalNbSeries / N < nbSeriesInChunk) N--; - // At this point N>=1 is the solution - idealNbSeriesInChunk = (double)totalNbSeries / N; - nbSeriesInChunk = ceil(idealNbSeriesInChunk); - } - - // Initialize random generator - init_rng(1); - - // Find out my identity in the default communicator - int myrank; - MPI_Comm_rank(MPI_COMM_WORLD, &myrank); - - if (myrank == 0) - { - // create temporary folder for intermediate results - mkdir(".tmp", S_IRWXU | S_IRGRP | S_IXGRP | S_IROTH | S_IXOTH); - - master_run(ifileName, totalNbSeries, nbSeriesInChunk, idealNbSeriesInChunk, - tsLength, nbClusters, randomize, p_for_dissims); - } - - else - slave_run(myrank, nbSeriesInChunk, nbClusters); - - MPI_Finalize(); - return 0; -} - -//main classification task (using clustering result) -//NOTE: ifileName == courbes à classer (?!) -int classif_main(int argc, char** argv) -{ - const char* ifileName = argv[1]; - const char* xmlFileName = argv[2]; - - // 1] load and retrieve info from XML file - xmlDoc* doc = xmlReadFile(xmlFileName, NULL, 0); - - // Get the root element node - xmlNode* root_element = xmlDocGetRootElement(doc); - - uint32_t p_for_dissims = 0; - uint32_t* ranks = NULL; - uint32_t nbClusters = 0; - char* binFileName; - for (xmlNode* cur_node=root_element->children; cur_node; cur_node=cur_node->next) - { - if (cur_node->type != XML_ELEMENT_NODE) - continue; - if (!strcmp(cur_node->name,"p_for_dissims")) - p_for_dissims = atoi(cur_node->last->content); - else if (!strcmp(cur_node->name,"ranks")) - { - //first pass: find nbClusters - for (xmlNode* rankNode=cur_node->children; rankNode; rankNode=rankNode->next) - { - if (rankNode->type == XML_ELEMENT_NODE && !strcmp(rankNode->name,"rank")) - nbClusters++; - } - //second pass: fill ranks (not optimal, but not very important here) - ranks = (uint32_t*) malloc(nbClusters*sizeof(uint32_t)); - uint32_t index = 0; - for (xmlNode* rankNode=cur_node->children; rankNode; rankNode=rankNode->next) - { - if (rankNode->type == XML_ELEMENT_NODE && !strcmp(rankNode->name,"rank")) - ranks[index++] = atoi(rankNode->last->content) - 1; - } - } - else if (!strcmp(cur_node->name,"file")) - { - binFileName = (char*) malloc(strlen(cur_node->last->content)+1); - strcpy(binFileName, cur_node->last->content); - } - } - - xmlFreeDoc(doc); - xmlCleanupParser(); - - uint32_t tsLength1 = get_tsLength(ifileName); - uint32_t tsLength2 = get_tsLength(binFileName); - if (tsLength1 != tsLength2) - { - fprintf(stderr,"Warning: nbValues do not match. Data will be truncated.\n"); - if (tsLength1 > tsLength2) - tsLength1 = tsLength2; - } - uint32_t nbValues = (tsLength1 - 4) / 4; - - // 2] Classify all series by batches of CURVES_PER_REQUEST - uint32_t nbSeries = get_nbSeries(ifileName); - PowerCurve* medoids = deserialize(binFileName, NULL, ranks, nbClusters); - free(binFileName); - free(ranks); - ranks = (uint32_t*)malloc(CURVES_PER_REQUEST*sizeof(uint32_t)); - - uint32_t smallestNonProcessedIndex = 0; - double DISTOR = 0.0; - FILE* labelsFile = fopen("LABELS", "w"); - while (smallestNonProcessedIndex < nbSeries) - { - uint32_t lowerBound = smallestNonProcessedIndex; - uint32_t upperBound = smallestNonProcessedIndex + CURVES_PER_REQUEST; - if (upperBound > nbSeries) - upperBound = nbSeries; - for (uint32_t i=0; i<upperBound-lowerBound; i++) - ranks[i] = lowerBound + i; - PowerCurve* data = deserialize(ifileName, NULL, ranks, upperBound-lowerBound); - uint32_t* labels = get_class(data, upperBound-lowerBound, medoids, nbClusters, - nbValues, p_for_dissims, &DISTOR); - // send labels to LABELS file - for (uint32_t i=0; i<upperBound-lowerBound; i++) - { - free(data[i].values); - fprintf(labelsFile, "%u\n",labels[i]); - } - free(data); - free(labels); - smallestNonProcessedIndex += (upperBound-lowerBound); - } - fclose(labelsFile); - for (uint32_t i=0; i<nbClusters; i++) - free(medoids[i].values); - free(medoids); - free(ranks); - fprintf(stdout, "DISTOR = %g\n",DISTOR); - return 0; -} - -int main(int argc, char** argv) -{ - if (argc <= 1) - { - fprintf(stderr, "No argument provided. Exit.\n"); - return 1; - } - - if (!strcmp(argv[1], "serialize")) - return serialize_main(argc-1, argv+1); - if (!strcmp(argv[1], "deserialize")) - return deserialize_main(argc-1, argv+1); - if (!strcmp(argv[1], "cluster")) - return cluster_main(argc-1, argv+1); - if (!strcmp(argv[1], "classif")) - return classif_main(argc-1, argv+1); - - fprintf(stderr, "Unknown first argument.\n"); - return 1; -} diff --git a/old_C_code/stage1/src/main.sh b/old_C_code/stage1/src/main.sh deleted file mode 100644 index 26cd32f..0000000 --- a/old_C_code/stage1/src/main.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/bin/bash -#PBS -l walltime=00:05:00 -#PBS -l nodes=2:ppn=8,pmem=1gb -#PBS -oe .output - -WORKDIR="/workdir2/auder/edfclust/code" -cd $WORKDIR - -rm -f .output - -saveLibPath=$LD_LIBRARY_PATH - -#TODO: CDS as a module -LD_LIBRARY_PATH=/workdir2/cds/lib:/opt/openmpi/lib:$LD_LIBRARY_PATH -export LD_LIBRARY_PATH - -#TODO: externalize (de)serialization, do not make assumptions on "how is data stored" (...) - -# ppam args (in case of clustering) = -# 1: path to binary dataset -# 2: series per processor -# 3: number of clusters -# 4: boolean to randomize series sampling -# 5: type of distance (1 for L1, 2 for L2...) - -#TODO: Hybrid MPI+OpenMP -mpirun -np 16 ppam cluster ../data/2009_first10000.bin 300 7 0 1 - -LD_LIBRARY_PATH=$saveLibPath -export LD_LIBRARY_PATH diff --git a/old_C_code/stage1/test/Algorithm/t.compute_coefficients.c b/old_C_code/stage1/test/Algorithm/t.compute_coefficients.c deleted file mode 100644 index 3ef3422..0000000 --- a/old_C_code/stage1/test/Algorithm/t.compute_coefficients.c +++ /dev/null @@ -1,100 +0,0 @@ -#include "Algorithm/compute_coefficients.h" -#include "Util/types.h" -#include "lut.h" -#include <math.h> -#include <stdlib.h> - -void t_compute_coefficients1() -{ - uint32_t nbSeries = 3; - uint32_t nbValues = 10; - Real curves[] = - { - 1.0,2.0,3.0,4.0,1.0,2.0,3.0,4.0,1.0,2.0, - 3.0,4.0,1.0,2.0,3.0,4.0,1.0,1.0,2.0,3.0, - 3.0,1.0,0.0,8.0,2.0,4.0,2.0,0.0,3.0,3.0 - }; - - PowerCurve* powerCurves = (PowerCurve*)malloc(nbSeries*sizeof(PowerCurve)); - for (int i=0; i<nbSeries; i++) - { - powerCurves[i].ID = i; - powerCurves[i].values = curves + i * nbValues; - } - - // Expected energy matrix: (to change...) - // 1.2829311 2.8197073 2.7653092 0.5390868 - // 1.433019 3.319643 2.078569 1.248209 - // 1.4532363 6.1433038 5.2052217 0.3447528 - - uint32_t nbReducedCoordinates = 4; //2^4 > 10, 2^3 < 10 - Real* reducedCoordinates = (Real*)malloc(nbSeries * nbReducedCoordinates * sizeof(Real)); - Real epsilon = 1e-4; - compute_coefficients(powerCurves, nbSeries, nbValues, reducedCoordinates, 0, nbReducedCoordinates); - free(powerCurves); - - //~ LUT_ASSERT(fabs(reducedCoordinates[0] - 1.2829311) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[1] - 2.8197073) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[2] - 2.7653092) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[3] - 0.5390868) < epsilon); -//~ - //~ LUT_ASSERT(fabs(reducedCoordinates[4] - 1.433019) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[5] - 3.319643) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[6] - 2.078569) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[7] - 1.248209) < epsilon); -//~ - //~ LUT_ASSERT(fabs(reducedCoordinates[8] - 1.4532363) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[9] - 6.1433038) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[10] - 5.2052217) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[11] - 0.3447528) < epsilon); - - free(reducedCoordinates); -} - -void t_compute_coefficients2() -{ - uint32_t nbSeries = 3; - uint32_t nbValues = 20; - Real curves[] = - { - 0.07291223,0.3468863,0.8648239,0.2348877,0.4315312,0.7036144,0.2431289,0.01040031,0.1178515,0.03080914,0.3673594,0.3738354,0.6695186,0.9140619,0.3102869,0.6374112,0.843919,0.2661967,0.1571974,0.7748992, - 0.8818654,0.6703627,0.8847847,0.03184918,0.997876,0.7612137,0.6387965,0.296034,0.5018912,0.7942868,0.1095461,0.3727642,0.2351644,0.5057783,0.7287164,0.340264,0.08904832,0.5050351,0.1371493,0.8821361, - 0.1844872,0.5861217,0.7114864,0.8779052,0.5999996,0.2707162,0.9586406,0.6902478,0.2514943,0.8113025,0.2820882,0.5661663,0.9571422,0.6838519,0.998652,0.6126693,0.9178886,0.7810725,0.7895782,0.181061 - }; - - PowerCurve* powerCurves = (PowerCurve*)malloc(nbSeries*sizeof(PowerCurve)); - for (int i=0; i<nbSeries; i++) - { - powerCurves[i].ID = i; - powerCurves[i].values = curves + i * nbValues; - } - - //~ // Expected energy matrix: - //~ // ...TODO - - uint32_t nbReducedCoordinates = 5; //2^5 > 20, 2^4 < 20 - Real* reducedCoordinates = (Real*)malloc(nbSeries * nbReducedCoordinates * sizeof(Real)); - Real epsilon = 1e-4; - compute_coefficients(powerCurves, nbSeries, nbValues, reducedCoordinates, 0, nbReducedCoordinates); - free(powerCurves); - - //~ LUT_ASSERT(fabs(reducedCoordinates[0] - 1.2829311) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[1] - 2.8197073) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[2] - 2.7653092) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[3] - 0.5390868) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[4] - 0.5390868) < epsilon); -//~ - //~ LUT_ASSERT(fabs(reducedCoordinates[5] - 3.319643) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[6] - 2.078569) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[7] - 1.248209) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[8] - 1.4532363) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[9] - 6.1433038) < epsilon); -//~ - //~ LUT_ASSERT(fabs(reducedCoordinates[10] - 5.2052217) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[11] - 0.3447528) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[12] - 0.3447528) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[13] - 1.248209) < epsilon); - //~ LUT_ASSERT(fabs(reducedCoordinates[14] - 1.248209) < epsilon); - - free(reducedCoordinates); -} diff --git a/old_C_code/stage1/test/Algorithm/t.pam.c b/old_C_code/stage1/test/Algorithm/t.pam.c deleted file mode 100644 index 06f1fe5..0000000 --- a/old_C_code/stage1/test/Algorithm/t.pam.c +++ /dev/null @@ -1,541 +0,0 @@ -#include "Algorithm/pam.h" -#include <stdlib.h> -#include "Util/types.h" -#include "lut.h" -#include <math.h> - -// 3 "regular flowers", dissimilarity = euclidian -void t_pam1() -{ - uint32_t nbClusters = 3; - Result_t* result = (Result_t*)malloc(sizeof(Result_t)); - result->medoids_ranks = (uint32_t*)malloc(nbClusters*sizeof(uint32_t)); - result->nbClusters = nbClusters; - -// double M[] = -// { -// -4,4, -// -3,5, -// -5,5, -// -5,3, -// -3,3, -// 0,0, -// 1,1, -// -1,1, -// -1,-1, -// 1,-1, -// 4,-4, -// 5,-3, -// 3,-3, -// 3,-5, -// 5,-5 -// }; - - uint32_t nbSeries = 15; - Real dissimilarities[] = - { - 0,1.414214,1.414214,1.414214,1.414214,5.656854,5.830952,4.242641,5.830952,7.071068,11.31371,11.40175,9.899495,11.40175,12.72792, - 1.414214,0,2,2.828427,2,5.830952,5.656854,4.472136,6.324555,7.211103,11.40175,11.31371,10,11.6619,12.80625, - 1.414214,2,0,2,2.828427,7.071068,7.211103,5.656854,7.211103,8.485281,12.72792,12.80625,11.31371,12.80625,14.14214, - 1.414214,2.828427,2,0,2,5.830952,6.324555,4.472136,5.656854,7.211103,11.40175,11.6619,10,11.31371,12.80625, - 1.414214,2,2.828427,2,0,4.242641,4.472136,2.828427,4.472136,5.656854,9.899495,10,8.485281,10,11.31371, - 5.656854,5.830952,7.071068,5.830952,4.242641,0,1.414214,1.414214,1.414214,1.414214,5.656854,5.830952,4.242641,5.830952,7.071068, - 5.830952,5.656854,7.211103,6.324555,4.472136,1.414214,0,2,2.828427,2,5.830952,5.656854,4.472136,6.324555,7.211103, - 4.242641,4.472136,5.656854,4.472136,2.828427,1.414214,2,0,2,2.828427,7.071068,7.211103,5.656854,7.211103,8.485281, - 5.830952,6.324555,7.211103,5.656854,4.472136,1.414214,2.828427,2,0,2,5.830952,6.324555,4.472136,5.656854,7.211103, - 7.071068,7.211103,8.485281,7.211103,5.656854,1.414214,2,2.828427,2,0,4.242641,4.472136,2.828427,4.472136,5.656854, - 11.31371,11.40175,12.72792,11.40175,9.899495,5.656854,5.830952,7.071068,5.830952,4.242641,0,1.414214,1.414214,1.414214,1.414214, - 11.40175,11.31371,12.80625,11.6619,10,5.830952,5.656854,7.211103,6.324555,4.472136,1.414214,0,2,2.828427,2, - 9.899495,10,11.31371,10,8.485281,4.242641,4.472136,5.656854,4.472136,2.828427,1.414214,2,0,2,2.828427, - 11.40175,11.6619,12.80625,11.31371,10,5.830952,6.324555,7.211103,5.656854,4.472136,1.414214,2.828427,2,0,2, - 12.72792,12.80625,14.14214,12.80625,11.31371,7.071068,7.211103,8.485281,7.211103,5.656854,1.414214,2,2.828427,2,0 - }; - - pam(dissimilarities, nbSeries, nbClusters, 0, NSTART, MAXITER, result); - // check that centers (medoids_ranks) span {0, 5, 10} - uint32_t* ctrs = result->medoids_ranks; - LUT_ASSERT(ctrs[0] != ctrs[1] && ctrs[1] != ctrs[2] && ctrs[2] != ctrs[0]); - LUT_ASSERT(ctrs[0] == 0 || ctrs[1] == 0 || ctrs[2] == 0); - LUT_ASSERT(ctrs[0] == 5 || ctrs[1] == 5 || ctrs[2] == 5); - LUT_ASSERT(ctrs[0] == 10 || ctrs[1] == 10 || ctrs[2] == 10); - - free(result->medoids_ranks); - free(result); -} - -// 4 well-separated Gaussian clusters, dissimilarity = euclidian -void t_pam2() -{ - uint32_t nbClusters = 4; - Result_t* result = (Result_t*)malloc(sizeof(Result_t)); - result->medoids_ranks = (uint32_t*)malloc(nbClusters*sizeof(uint32_t)); - result->nbClusters = nbClusters; - -// double M[] = -// { -// -4.450371,-4.543001, -// -4.711869,-4.961091, -// -5.083656,-4.674564, -// -3.586104,-5.887045, -// -3.185154,-3.541619, -// -5.033892,-4.596002, -// -3.004485,-5.171559, -// -4.528517,-4.003823, -// -3.721287,-5.419952, -// -4.121808,-4.629665, -// -4.05129,-4.85515, -// -4.035312,-5.660147, -// -3.290654,-4.240134, -// -1.902941,-4.663231, -// -3.070627,-3.537133, -// -5.799731,-6.077648, -// -2.999374,-4.396087, -// -3.826953,-3.367948, -// -6.482025,-2.427264, -// -5.412339,-3.98535, -// -3.37746,-3.841064, -// -4.712918,-3.058541, -// -2.440538,-4.408247, -// -3.116167,-6.130969, -// -4.604153,-3.708026, -// -3.472353,-3.137196, -// -3.843901,-3.447817, -// -3.340786,-2.312214, -// -4.328582,-4.133865, -// -2.906439,-5.249392, -// -3.55544,-4.535076, -// -4.346427,-4.156633, -// -2.092725,-4.128729, -// -5.015867,-5.179254, -// -4.543845,-2.916083, -// -4.145448,-5.715884, -// -4.390567,-2.307922, -// -3.003025,-5.588462, -// -2.844272,-4.292232, -// -3.51101,-5.665364, -// -4.669003,-3.983029, -// -4.040262,-3.338078, -// -4.002328,-3.652771, -// -3.732904,-6.027206, -// -4.094534,-3.500277, -// -3.587201,-4.003284, -// -4.816506,-4.769324, -// -3.062788,-3.027322, -// -2.899946,-6.049017, -// -4.912952,-4.773183, -// -1.916896,-0.009493825, -// 2.634858,-0.4243543, -// 0.3020768,1.64798, -// 1.319387,0.4383175, -// -0.001776739,-0.3686283, -// -0.3792117,-0.614869, -// -0.7154037,-0.6211681, -// -1.422143,0.2300399, -// 0.1448215,0.2528107, -// -0.665172,-1.395703, -// 1.447993,0.2860408, -// -0.3756387,-0.06096208, -// -1.001823,-0.2453904, -// -0.05310829,-2.201463, -// -0.6424461,-0.4816693, -// 0.9660234,-0.6773616, -// 0.3242067,0.6265039, -// -1.54016,0.3410867, -// -0.151697,0.9412011, -// -0.1419021,1.402804, -// 0.6756108,-2.120431, -// -0.4478024,-0.7631725, -// 0.640236,1.548211, -// 1.094596,1.688888, -// -0.4296057,-0.3780608, -// -0.3501862,-0.4554224, -// -0.7654625,-0.5809421, -// -0.3401964,-0.455078, -// 1.656617,-0.7471582, -// 1.436579,-0.3640174, -// 0.2339464,0.3475537, -// 0.08126267,1.06486, -// 0.2915553,-0.236505, -// 0.4851901,-1.010106, -// -0.07553644,-2.15231, -// -0.4303397,0.9054603, -// 0.004220171,1.033742, -// -0.3602884,1.916468, -// -0.4638079,-1.552088, -// -1.60057,-1.106041, -// -0.7134006,-1.567929, -// -1.337527,-3.620209, -// -0.1994646,-0.1307016, -// -0.2420102,-0.1121753, -// -0.536283,0.03036439, -// 2.467503,0.3452919, -// -0.3630185,1.174053, -// 0.1055285,-0.2248235, -// 1.267688,-0.3149153, -// -1.620448,-0.6095385, -// 5.14672,4.479654, -// 4.129114,5.259524, -// 5.365162,3.888272, -// 3.454389,2.743306, -// 2.861091,2.602548, -// 3.93366,4.617158, -// 4.57284,3.714136, -// 4.092174,3.671644, -// 4.50261,1.516262, -// 4.317581,3.683757, -// 4.259846,2.438663, -// 4.119361,6.446651, -// 5.691461,3.578193, -// 3.861858,3.574928, -// 3.537764,4.3348, -// 2.586984,4.144694, -// 4.565765,3.690908, -// 4.949011,3.678565, -// 4.21298,4.516202, -// 4.258206,3.375235, -// 3.862015,3.229653, -// 2.276514,3.281311, -// 3.461244,5.803409, -// 5.741595,4.279209, -// 3.847961,3.979272, -// 2.904032,5.174558, -// 4.973815,3.879027, -// 2.342122,2.615265, -// 2.576959,5.048676, -// 4.996508,3.129088, -// 4.908488,5.593342, -// 4.494416,4.222817, -// 6.292328,2.357233, -// 3.34451,3.270198, -// 2.269291,3.963629, -// 4.25654,3.247725, -// 2.894098,3.785049, -// 2.346439,4.385733, -// 5.85847,6.073889, -// 1.42734,4.093241, -// 4.07797,2.895063, -// 4.444542,2.00364, -// 5.394777,5.063008, -// 2.639821,4.398103, -// 4.273526,2.889649, -// 3.477858,5.404986, -// 5.729747,3.629496, -// 3.810896,4.770437, -// 3.71945,5.236858, -// 3.833841,3.280717, -// 4.152189,-5.99135, -// 3.19188,-5.967943, -// 5.042668,-6.489929, -// 5.237402,-4.916319, -// 5.335625,-3.950791, -// 4.027703,-4.435844, -// 3.654064,-5.122268, -// 5.109319,-3.817966, -// 5.21631,-4.550999, -// 4.518432,-6.290373, -// 4.224753,-3.722504, -// 4.315041,-4.396765, -// 5.56141,-4.3007, -// 4.428661,-3.642649, -// 3.759849,-5.697695, -// 3.478439,-5.001031, -// 4.693304,-5.403166, -// 4.822672,-3.103692, -// 4.885982,-3.900518, -// 5.715825,-4.795573, -// 6.714095,-5.538454, -// 4.872663,-6.734268, -// 5.04218,-6.48153, -// 5.145617,-4.279071, -// 3.550852,-5.84931, -// 4.806485,-4.733755, -// 4.280439,-4.219329, -// 4.628427,-5.355216, -// 4.133676,-5.390665, -// 4.283339,-4.694953, -// 5.662802,-4.493008, -// 3.031885,-4.058671, -// 4.413487,-4.646903, -// 6.535346,-4.918966, -// 3.408803,-3.448405, -// 4.701184,-6.628794, -// 5.478372,-4.380599, -// 5.902022,-6.036273, -// 4.379025,-2.736512, -// 4.590189,-3.72093, -// 4.625894,-5.511869, -// 5.09324,-4.854936, -// 5.402295,-7.326092, -// 7.355636,-3.345965, -// 4.646836,-5.001939, -// 5.450266,-7.242952, -// 4.197233,-5.91857, -// 3.848639,-4.45381, -// 5.578822,-4.595297, -// 5.741669,-4.29464 -// }; - - uint32_t nbSeries = 200; - Real dissimilarities[] = - { - 0,0.4931329,0.6468061,1.59794,1.61355,0.5859224,1.576601,0.5448115,1.140442,0.3398006,0.5066584,1.191759,1.198613,2.550266,1.707473,2.043505,1.458416,1.330188,2.933251,1.111916,1.282129,1.507499,2.014345,2.074064,0.8490186,1.712545,1.251893,2.491503,0.4268785,1.697855,0.8949661,0.4001053,2.393766,0.8512363,1.629601,1.211871,2.235879,1.785441,1.625559,1.463591,0.6011401,1.272803,0.9966205,1.648521,1.101768,1.018016,0.4304379,2.054914,2.161458,0.5166866,5.193379,8.195347,7.804742,7.622575,6.100441,5.657257,5.415788,5.652618,6.641958,4.922725,7.623015,6.057402,5.510167,4.981839,5.567289,6.654359,7.037072,5.685389,6.968147,7.342718,5.669615,5.505239,7.938334,8.341648,5.789066,5.78963,5.410773,5.796952,7.190531,7.219424,6.772028,7.20998,6.405605,6.069688,4.985437,6.770996,7.137468,7.64549,4.983799,4.464758,4.77661,3.246744,6.126875,6.110853,6.019614,8.470679,7.027884,6.277171,7.111463,4.845678,13.17241,13.02678,12.93952,10.7506,10.22332,12.41775,12.23105,11.85139,10.81067,12.02317,11.16295,13.93602,12.99271,11.61869,11.9426,11.18036,12.21016,12.4877,12.53486,11.77021,11.38024,10.31847,13.02466,13.47989,11.89502,12.1868,12.63906,9.868066,11.89049,12.16982,13.79615,12.52393,12.76788,11.03659,10.84051,11.68357,11.10395,11.22136,14.79833,10.44663,11.31625,11.04436,13.75508,11.41114,11.46083,12.72082,13.05467,12.44944,12.7433,11.39468,8.723632,7.773961,9.690631,9.694963,9.803899,8.478751,8.125111,9.587145,9.666684,9.137436,8.71384,8.766631,10.01471,8.924564,8.291021,7.942029,9.184045,9.384079,9.358433,10.16933,11.20876,9.577088,9.688468,9.599617,8.107158,9.258822,8.736808,9.115057,8.625799,8.735032,10.1133,7.497916,8.864467,10.99215,7.935033,9.38624,9.930072,10.45954,9.012305,9.077859,9.127831,9.548708,10.23819,11.86654,9.108776,10.26218,8.756327,8.299489,10.02933,10.19507, - 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10.19507,10.47476,10.83199,9.462721,8.958527,10.77977,8.790005,10.2743,9.529631,9.869165,9.808987,9.871878,9.032487,7.65349,8.844794,11.67831,8.741631,9.613391,12.36551,11.15829,9.130402,10.52741,8.182996,9.04618,10.36244,9.286435,9.622903,9.29629,10.07153,8.700651,9.300218,10.08904,7.83615,10.79385,10.37749,9.988744,10.32518,8.83989,8.585941,9.353659,10.41533,9.82859,9.765115,9.631683,9.868227,9.333418,10.56884,8.895198,8.817899,10.66536,8.775881,4.963002,8.056295,6.477458,6.957064,7.141841,7.428875,8.473071,7.21138,7.032172,6.278398,7.439454,7.865819,6.161236,7.436106,5.990951,7.318917,8.632206,7.88326,8.190072,5.512906,7.126066,7.756515,7.57614,7.309187,7.200714,7.492283,7.192449,5.410386,5.829545,7.203122,7.795154,6.795013,6.198285,6.199149,8.070609,7.830069,8.706994,6.784507,8.004726,7.007344,7.111249,7.255029,7.300508,7.623539,5.678832,8.195963,6.951941,5.987881,8.232906,8.794441,9.689292,8.191569,7.400292,7.474552,9.093351,8.093618,8.135263,5.941536,8.104495,6.89443,10.86311,7.872993,8.09097,8.906427,9.009683,8.071662,8.012509,8.942473,7.812019,7.755519,8.330806,10.35234,8.573848,8.487859,9.885236,8.209654,7.70089,9.864732,7.461032,9.923022,8.608293,6.674627,7.935562,8.958594,7.687189,8.566798,9.320755,10.36919,9.43239,7.379683,6.430464,9.364075,9.229585,7.332765,9.9603,7.924144,9.268414,9.743656,7.811903,2.324925,3.049814,2.303887,0.8004816,0.5320745,1.719773,2.245676,0.7918867,0.5845699,2.340782,1.621226,1.430279,0.1803605,1.465975,2.428204,2.370906,1.525745,1.504298,0.9420895,0.5015995,1.578824,2.58978,2.296034,0.5962549,2.686387,1.033146,1.463169,1.537572,1.945999,1.512275,0.2134713,2.720038,1.374102,1.009805,2.481608,2.55556,0.2769731,1.749,2.069919,1.286487,1.651242,0.8569664,3.050389,1.872131,1.30343,2.962678,2.241078,1.89971,0.3419266,0 - }; - - pam(dissimilarities, nbSeries, nbClusters, 0, NSTART, MAXITER, result); - - // Label items - uint32_t* labels = (uint32_t*)malloc(nbSeries*sizeof(uint32_t)); - uint32_t* ctrs = result->medoids_ranks; - - // DEBUG: - //printf("CTRS: %f,%f %f,%f %f,%f %f,%f\n",N[2*ctrs[0]],N[2*ctrs[0]+1],N[2*ctrs[1]],N[2*ctrs[1]+1],N[2*ctrs[2]],N[2*ctrs[2]+1],N[2*ctrs[3]],N[2*ctrs[3]+1]); - - for (uint32_t i=0; i<nbSeries; i++) - { - Real minDissim = INFINITY; - uint32_t bestClust = 0; - for (uint32_t j=0; j<nbClusters; j++) - { - if (dissimilarities[i*nbSeries + ctrs[j]] < minDissim) - { - minDissim = dissimilarities[i*nbSeries + ctrs[j]]; - bestClust = j; - } - } - labels[i] = bestClust; - } - - // Check that clusters composition match 'true distribution' well enough - - // Tolerance == +/- 3 series - double maxDeltaSeries = 3.0; - // labels balancing: - for (uint32_t j=0; j<nbClusters; j++) - { - uint32_t countClusterLabels = 0; - for (uint32_t i=0; i<nbSeries; i++) - { - if (labels[i] == j) - countClusterLabels++; - } - LUT_ASSERT(fabs(countClusterLabels - 50.0) <= maxDeltaSeries); - } - - // clusters purity: - for (uint32_t j=0; j<nbClusters; j++) - { - uint32_t labelsCount[] = {0,0,0,0}; - for (uint32_t i=50*j; i<50*j+50; i++) - labelsCount[labels[i]]++; - uint32_t maxLabelsCount = 0; - for (uint32_t jj=0; jj<nbClusters; jj++) - { - if (labelsCount[jj] > maxLabelsCount) - maxLabelsCount = labelsCount[jj]; - } - LUT_ASSERT(50 - maxLabelsCount <= maxDeltaSeries); - } - - free(result->medoids_ranks); - free(labels); - free(result); -} diff --git a/old_C_code/stage1/test/CMakeLists.txt b/old_C_code/stage1/test/CMakeLists.txt deleted file mode 100644 index 7b89bed..0000000 --- a/old_C_code/stage1/test/CMakeLists.txt +++ /dev/null @@ -1,19 +0,0 @@ -cmake_minimum_required(VERSION 2.8.5) -project(ppam_mpi_tests) - -set(CMAKE_C_FLAGS "-std=gnu99") -set(CMAKE_C_FLAGS_DEBUG "-g3") -set(CMAKE_C_FLAGS_RELEASE "-O3") - -#already by default... ?! -include_directories("${CMAKE_SOURCE_DIR}") - -#ppam tests - -include_directories("${CMAKE_SOURCE_DIR}/../src") - -file(GLOB_RECURSE test_ppam_src "${CMAKE_SOURCE_DIR}/*.c") -add_executable(test_ppam.exe ${test_ppam_src}) -find_library(PPAM_LIBRARY ppam "${CMAKE_BINARY_DIR}") -#TODO: Rblas ?! -target_link_libraries(test_ppam.exe m cds ${PPAM_LIBRARY}) diff --git a/old_C_code/stage1/test/MPI_Communication/t.pack.c b/old_C_code/stage1/test/MPI_Communication/t.pack.c deleted file mode 100644 index 6692af5..0000000 --- a/old_C_code/stage1/test/MPI_Communication/t.pack.c +++ /dev/null @@ -1,106 +0,0 @@ -#include "lut.h" -#include <stdlib.h> -#include "MPI_Communication/pack.h" -#include "Util/types.h" -#include <stdint.h> -#include "Util/utils.h" - -// Work_t -void t_pack1() -{ - Work_t* work = (Work_t*)malloc(sizeof(Work_t)); - work->inputFileName = "../tdata/inputTest.bin"; - work->nbSeries = 5; - uint32_t ranks[] = - { - 0, - 12*65536 + 3*256+11, - 32*16777216 + 2*65536 + 17*256 + 42, - 123, - 51*16777216 + 77*65536 + 0*256 + 10 - }; - work->ranks = ranks; - work->nbClusters = 15; - work->clustOnMedoids = 0; - work->p_for_dissims = 1; - - // Hard-coded expected packed work - unsigned char expectedPackedWork[] = - { - // --> ../tdata/inputTest.bin - 46,46,47,100,97,116,97,47,105,110,112,117,116,84,101,115,116,46,98,105,110, - // continue with 256 - strlen("../tdata/inputTest.bin") zeros... - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - // --> 5, {ranks}+0,0, 15, 0 on 4-bytes integers little-endian - 5,0,0,0, 0,0,0,0, 11,3,12,0, 42,17,2,32, 123,0,0,0, 10,0,77,51, 0,0,0,0, 0,0,0,0, - 15,0,0,0, 0,0,0,0, 1,0,0,0 - }; - - uint32_t nbSeriesInChunk = 7; - uint32_t work_message_length = get_packedWork_length(nbSeriesInChunk); - unsigned char packedWork[work_message_length]; - pack_work(work, nbSeriesInChunk, packedWork); - - for (uint32_t i=0; i<work_message_length; i++) - { - LUT_ASSERT(packedWork[i] == expectedPackedWork[i]); - } - - free(work); -} - -// Result_t -void t_pack2() { - - Result_t* result = (Result_t*)malloc(sizeof(Result_t)); - uint32_t nbClusters = result->nbClusters = 5; - uint32_t medoids_ID[] = - { - 15*65536 + 13*256+11, - 11, - 17*16777216 + 0*65536 + 14*256 + 42, - 123*65536 + 0*256 + 7, - 51*16777216 + 0*65536 + 0*256 + 10 - }; - result->medoids_ID = medoids_ID; - uint32_t medoids_ranks[] = - { - 5*65536 + 32, - 11*256 + 4, - 23*16777216 + 42, - 35*65536 + 5*256 + 77, - 1*16777216 + 1*65536 + 1*256 + 10 - }; - result->medoids_ranks = medoids_ranks; - - // Hard-coded expected result - unsigned char expectedPackedResult[] = - { - // 5, {medoids_ID}, {medoids_ranks} - 5,0,0,0, - 11,13,15,0, 11,0,0,0, 42,14,0,17, 7,0,123,0, 10,0,0,51, - 32,0,5,0, 4,11,0,0, 42,0,0,23, 77,5,35,0, 10,1,1,1 - }; - - uint32_t result_message_length = get_packedResult_length(nbClusters); - unsigned char packedResult[result_message_length]; - pack_result(result, packedResult); - - for (uint32_t i=0; i<result_message_length; i++) - { - LUT_ASSERT(packedResult[i] == expectedPackedResult[i]); - } - - free(result); -} diff --git a/old_C_code/stage1/test/MPI_Communication/t.unpack.c b/old_C_code/stage1/test/MPI_Communication/t.unpack.c deleted file mode 100644 index caf0e8d..0000000 --- a/old_C_code/stage1/test/MPI_Communication/t.unpack.c +++ /dev/null @@ -1,97 +0,0 @@ -#include "lut.h" -#include <stdlib.h> -#include "MPI_Communication/unpack.h" -#include "Util/types.h" -#include <stdint.h> -#include <string.h> -#include "Util/utils.h" - -// Work_t -void t_unpack1() -{ - // Hard-coded packed work - unsigned char packedWork[] = - { - // --> ../tdata/inputTest.bin - 46,46,47,100,97,116,97,47,105,110,112,117,116,84,101,115,116,46,98,105,110, - // continue with 256 - strlen("../tdata/inputTest.bin") zeros... - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, - // --> 5, {ranks}+0,0, 15, 0 on 4-bytes integers little-endian - 5,0,0,0, 0,0,0,0, 11,3,12,0, 42,17,2,32, 123,0,0,0, 10,0,77,51, 0,0,0,0, 0,0,0,0, - 15,0,0,0, 0,0,0,0, 2,0,0,0 - }; - - uint32_t nbSeriesInChunk = 7; - Work_t* work = unpack_work(packedWork, nbSeriesInChunk); - - LUT_ASSERT(strcmp(work->inputFileName, "../tdata/inputTest.bin") == 0); - LUT_ASSERT(work->nbSeries == 5); - uint32_t ranks[] = - { - 0, - 12*65536 + 3*256+11, - 32*16777216 + 2*65536 + 17*256 + 42, - 123, - 51*16777216 + 77*65536 + 0*256 + 10 - }; - for (uint32_t i=0; i < work->nbSeries; i++) - { - LUT_ASSERT(work->ranks[i] == ranks[i]); - } - LUT_ASSERT(work->nbClusters == 15); - LUT_ASSERT(work->clustOnMedoids == 0); - LUT_ASSERT(work->p_for_dissims == 2); - - free_work(work); -} - -// Result_t -void t_unpack2() { - - // Hard-coded packed result - unsigned char packedResult[] = - { - // 5, {medoids_ID}, {medoids_ranks} - 5,0,0,0, - 11,13,15,0, 11,0,0,0, 42,14,0,17, 7,0,123,0, 10,0,0,51, - 32,0,5,0, 4,11,0,0, 42,0,0,23, 77,5,35,0, 10,1,1,1 - }; - - Result_t* result = unpack_result(packedResult); - - LUT_ASSERT(result->nbClusters == 5); - uint32_t medoids_ID[] = - { - 15*65536 + 13*256+11, - 11, - 17*16777216 + 0*65536 + 14*256 + 42, - 123*65536 + 0*256 + 7, - 51*16777216 + 0*65536 + 0*256 + 10 - }; - uint32_t medoids_ranks[] = - { - 5*65536 + 32, - 11*256 + 4, - 23*16777216 + 42, - 35*65536 + 5*256 + 77, - 1*16777216 + 1*65536 + 1*256 + 10 - }; - for (uint32_t i=0; i<result->nbClusters; i++) - { - LUT_ASSERT(result->medoids_ID[i] == medoids_ID[i]); - LUT_ASSERT(result->medoids_ranks[i] == medoids_ranks[i]); - } - - free_result(result); -} diff --git a/old_C_code/stage1/test/TimeSeries/t.deserialize.c b/old_C_code/stage1/test/TimeSeries/t.deserialize.c deleted file mode 100644 index 3a6413d..0000000 --- a/old_C_code/stage1/test/TimeSeries/t.deserialize.c +++ /dev/null @@ -1,94 +0,0 @@ -#include "TimeSeries/deserialize.h" -#include "lut.h" -#include <math.h> -#include <stdlib.h> -#include <stdio.h> -#include "Util/utils.h" - -void t_deserialize1() -{ - // decode sample_byCols.bin into the toy dataset (4 customers, 3 sample points) - const char* ifName = "../tdata/test/sample_byCols.bin"; - - // check 'header' - uint32_t nbSeries = get_nbSeries(ifName); - LUT_ASSERT(nbSeries == 4); - uint32_t tsLength = get_tsLength(ifName); - LUT_ASSERT(tsLength == 13); //3*3+4 - - uint32_t ranks[] = - { - 0, 2, 1, 3 - }; - PowerCurve* powerCurves = deserialize(ifName, NULL, ranks, nbSeries); - - Real epsilon = 0.1; //epsilon = 0.1 because raw powers are truncated - - LUT_ASSERT(powerCurves[0].ID == 12301); - LUT_ASSERT(fabs(powerCurves[0].values[0] - 23.051) < epsilon); - LUT_ASSERT(fabs(powerCurves[0].values[1] - 33.052) < epsilon); - LUT_ASSERT(fabs(powerCurves[0].values[2] - 43.053) < epsilon); - - LUT_ASSERT(powerCurves[1].ID == 1313); - LUT_ASSERT(fabs(powerCurves[1].values[0] - 50.05) < epsilon); - LUT_ASSERT(fabs(powerCurves[1].values[1] - 51.05) < epsilon); - LUT_ASSERT(fabs(powerCurves[1].values[2] - 52.05) < epsilon); - - LUT_ASSERT(powerCurves[2].ID == 50000); - LUT_ASSERT(fabs(powerCurves[2].values[0] - 150.321) < epsilon); - LUT_ASSERT(fabs(powerCurves[2].values[1] - 160.322) < epsilon); - LUT_ASSERT(fabs(powerCurves[2].values[2] - 140.323) < epsilon); - - LUT_ASSERT(powerCurves[3].ID == 6300); - LUT_ASSERT(fabs(powerCurves[3].values[0] - 500.30) < epsilon); - LUT_ASSERT(fabs(powerCurves[3].values[1] - 501.31) < epsilon); - LUT_ASSERT(fabs(powerCurves[3].values[2] - 502.32) < epsilon); - - for (int i = 0; i < nbSeries; i++) - free(powerCurves[i].values); - free(powerCurves); -} - -void t_deserialize2() -{ - // decode sample_byRows.bin into the toy dataset (4 customers, 3 sample points) - const char* ifName = "../tdata/test/sample_byRows.bin"; - - // check 'header' - uint32_t nbSeries = get_nbSeries(ifName); - LUT_ASSERT(nbSeries == 4); - uint32_t tsLength = get_tsLength(ifName); - LUT_ASSERT(tsLength == 13); //3*3+4 - - uint32_t ranks[] = - { - 0, 2, 1, 3 - }; - PowerCurve* powerCurves = deserialize(ifName, NULL, ranks, nbSeries); - - Real epsilon = 0.1; //epsilon = 0.1 because raw powers are truncated - - LUT_ASSERT(powerCurves[0].ID == 12301); - LUT_ASSERT(fabs(powerCurves[0].values[0]/100.0-3 - 23.051) < epsilon); - LUT_ASSERT(fabs(powerCurves[0].values[1]/100.0-3 - 33.052) < epsilon); - LUT_ASSERT(fabs(powerCurves[0].values[2]/100.0-3 - 43.053) < epsilon); - - LUT_ASSERT(powerCurves[1].ID == 1313); - LUT_ASSERT(fabs(powerCurves[1].values[0]/100.0-3 - 50.05) < epsilon); - LUT_ASSERT(fabs(powerCurves[1].values[1]/100.0-3 - 51.05) < epsilon); - LUT_ASSERT(fabs(powerCurves[1].values[2]/100.0-3 - 52.05) < epsilon); - - LUT_ASSERT(powerCurves[2].ID == 50000); - LUT_ASSERT(fabs(powerCurves[2].values[0]/100.0-3 - 150.321) < epsilon); - LUT_ASSERT(fabs(powerCurves[2].values[1]/100.0-3 - 160.322) < epsilon); - LUT_ASSERT(fabs(powerCurves[2].values[2]/100.0-3 - 140.323) < epsilon); - - LUT_ASSERT(powerCurves[3].ID == 6300); - LUT_ASSERT(fabs(powerCurves[3].values[0]/100.0-3 - 500.30) < epsilon); - LUT_ASSERT(fabs(powerCurves[3].values[1]/100.0-3 - 501.31) < epsilon); - LUT_ASSERT(fabs(powerCurves[3].values[2]/100.0-3 - 502.32) < epsilon); - - for (int i = 0; i < nbSeries; i++) - free(powerCurves[i].values); - free(powerCurves); -} diff --git a/old_C_code/stage1/test/TimeSeries/t.serialize.c b/old_C_code/stage1/test/TimeSeries/t.serialize.c deleted file mode 100644 index 6003c67..0000000 --- a/old_C_code/stage1/test/TimeSeries/t.serialize.c +++ /dev/null @@ -1,47 +0,0 @@ -#include "TimeSeries/serialize.h" -#include "lut.h" -#include <unistd.h> -#include <stdio.h> - -static void checkFilesEqual(const char* fileName1, const char* fileName2) -{ - FILE* output = fopen(fileName1, "rb"); - FILE* refOutput = fopen(fileName2, "rb"); - while (!feof(output) && !feof(refOutput)) - LUT_ASSERT(fgetc(output) == fgetc(refOutput)); - LUT_ASSERT(feof(output) && feof(refOutput)); - fclose(output); - fclose(refOutput); -} - -void t_serialize1() -{ - const char* csvIfName = "../tdata/test/sample_byCols.csv"; - const char* binaryIfName = "../tdata/test/sample_byCols.bin"; - const char* tmpBinaryIfName = "../tdata/test/sample_byCols.tmp.bin"; - - // serialize text file into a temporary binary file - serialize_byCols(csvIfName, tmpBinaryIfName, 0); - - // compare binary result with reference - checkFilesEqual(tmpBinaryIfName, binaryIfName); - - // remove temp file - unlink(tmpBinaryIfName); -} - -void t_serialize2() -{ - const char* csvIfName = "../tdata/test/sample_byRows.csv"; - const char* binaryIfName = "../tdata/test/sample_byRows.bin"; - const char* tmpBinaryIfName = "../tdata/test/sample_byRows.tmp.bin"; - - // serialize text file into a temporary binary file - serialize_byRows(csvIfName, tmpBinaryIfName, 0); - - // compare binary result with reference - checkFilesEqual(tmpBinaryIfName, binaryIfName); - - // remove temp file - unlink(tmpBinaryIfName); -} diff --git a/old_C_code/stage1/test/Util/t.rng.c b/old_C_code/stage1/test/Util/t.rng.c deleted file mode 100644 index ee98b98..0000000 --- a/old_C_code/stage1/test/Util/t.rng.c +++ /dev/null @@ -1,49 +0,0 @@ -#include "lut.h" -#include "Util/rng.h" -#include <stdlib.h> -#include <cds/Vector.h> -#include <math.h> - -// Auxiliary to perform K-S test for a given flag, sample size and bins count -void aux_ks_test(int flag, uint32_t N, uint32_t nBins) -{ - init_rng(flag); - - // Fill the bins - uint32_t bins[nBins]; - for (uint32_t i=0; i<nBins; i++) - bins[i] = 0; - for (uint32_t i=0; i<N; i++) - { - Real rf = get_rand_real(); - uint32_t index = floor(rf*nBins); - if (index >= nBins) index = nBins - 1; //in case of... - bins[index]++; - } - - // Test the bins - double ksThreshold = 1.358 / sqrt((double)N); - double countPerBin = (double)N / nBins; - uint32_t cumulativeSum = 0; - for (uint32_t i=0; i<nBins; i++) - { - cumulativeSum += bins[i]; - LUT_ASSERT((double)cumulativeSum / N - (i+1)*countPerBin/N < ksThreshold); - } -} - -// Kolmogorov-Smirnov test on random real numbers (flag==0) -void t_rng1() -{ - aux_ks_test(0, 1000000, 1000); - aux_ks_test(0, 100000, 1000); - aux_ks_test(0, 10000, 100); -} - -// Kolmogorov-Smirnov test on random real numbers (flag==1) -void t_rng2() -{ - aux_ks_test(1, 1000000, 1000); - aux_ks_test(1, 100000, 1000); - aux_ks_test(1, 10000, 100); -} diff --git a/old_C_code/stage1/test/Util/t.utils.c b/old_C_code/stage1/test/Util/t.utils.c deleted file mode 100644 index 1abed91..0000000 --- a/old_C_code/stage1/test/Util/t.utils.c +++ /dev/null @@ -1,50 +0,0 @@ -#include "lut.h" -#include "Util/utils.h" -#include <stdlib.h> -#include <stdio.h> -#include "Util/types.h" -#include <math.h> - -//integers -void t_utils1() -{ - FILE* file = fopen("../tdata/test/integers.txt", "rb"); - int64_t x; - readInt(file, &x); - LUT_ASSERT(x == 1234); - readInt(file, &x); - LUT_ASSERT(x == -987); - readInt(file, &x); - LUT_ASSERT(x == 654321); - readInt(file, &x); - LUT_ASSERT(x == 12); - readInt(file, &x); - LUT_ASSERT(x == 345678); - readInt(file, &x); - LUT_ASSERT(x == -1234); - fclose(file); -} - -//reals -void t_utils2() -{ - FILE* file = fopen("../tdata/test/reals.txt", "rb"); - Real x, tol = 1e-10; - readReal(file, &x); - LUT_ASSERT(fabs(x - 1234.056) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - 987e-3) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - -654321e-2) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - 12.34567) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - 345678.0) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - -1234.05) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - 3e-1) < tol); - readReal(file, &x); - LUT_ASSERT(fabs(x - -1.0188) < tol); - fclose(file); -} diff --git a/old_C_code/stage1/test/lut.h b/old_C_code/stage1/test/lut.h deleted file mode 100644 index 4e36e51..0000000 --- a/old_C_code/stage1/test/lut.h +++ /dev/null @@ -1,15 +0,0 @@ -#ifndef PPAM_LUT_H -#define PPAM_LUT_H - -#include <stdio.h> - -// light, minimalist unit-testing "framework" - -#define LUT_ASSERT(condition) \ - do { \ - if ( !(condition) ) { \ - printf("Failure in file %s at line %i\n",__FILE__,__LINE__); \ - } \ - } while (0) - -#endif diff --git a/old_C_code/stage1/test/main.c b/old_C_code/stage1/test/main.c deleted file mode 100644 index 3d80af6..0000000 --- a/old_C_code/stage1/test/main.c +++ /dev/null @@ -1,39 +0,0 @@ -void t_serialize1(); -void t_serialize2(); -void t_deserialize1(); -void t_deserialize2(); -void t_compute_coefficients1(); -void t_compute_coefficients2(); -void t_pam1(); -void t_pam2(); -void t_pack1(); -void t_pack2(); -void t_unpack1(); -void t_unpack2(); -void t_rng1(); -void t_rng2(); -void t_utils1(); -void t_utils2(); - -// list all unit tests here -int main() -{ - t_serialize1(); - t_serialize2(); - t_deserialize1(); - t_deserialize2(); - //~ t_compute_coefficients1(); - //~ t_compute_coefficients2(); - t_pam1(); //may fail - t_pam2(); - t_pack1(); - t_pack2(); - t_unpack1(); - t_unpack2(); - t_rng1(); //may fail - t_rng2(); - t_utils1(); - t_utils2(); - - return 0; -} diff --git a/old_C_code/stage1/test/tdata/integers.txt b/old_C_code/stage1/test/tdata/integers.txt deleted file mode 100644 index 4803f02..0000000 --- a/old_C_code/stage1/test/tdata/integers.txt +++ /dev/null @@ -1,3 +0,0 @@ -1234,-987,654321,12 -345678 --1234 diff --git a/old_C_code/stage1/test/tdata/reals.txt b/old_C_code/stage1/test/tdata/reals.txt deleted file mode 100644 index ef894e6..0000000 --- a/old_C_code/stage1/test/tdata/reals.txt +++ /dev/null @@ -1,4 +0,0 @@ -1234.056,987e-3,-654321E-2,12.34567 -345678 --1234.05 -3.0000000e-001,-1.0188000e+000 diff --git a/old_C_code/stage1/test/tdata/sample_byCols.bin b/old_C_code/stage1/test/tdata/sample_byCols.bin deleted file mode 100644 index effa00e..0000000 Binary files a/old_C_code/stage1/test/tdata/sample_byCols.bin and /dev/null differ diff --git a/old_C_code/stage1/test/tdata/sample_byCols.csv b/old_C_code/stage1/test/tdata/sample_byCols.csv deleted file mode 100644 index 55d304e..0000000 --- a/old_C_code/stage1/test/tdata/sample_byCols.csv +++ /dev/null @@ -1,13 +0,0 @@ -FK_CCU_ID,CPP_DATE_PUISSANCE,CPP_PUISSANCE_CORRIGEE,CPP_PUISSANCE_BRUTE -12301,12JAN2009:00:30:00,,23.051 -12301,12JAN2009:01:00:00,,33.052 -12301,12JAN2009:01:30:00,32.32,43.053 -50000,23MAR2009:00:30:00,,150.321 -50000,23MAR2009:01:00:00,,160.322 -50000,23MAR2009:01:30:00,11.1,140.323 -1313,30MAY2009:10:30:00,10.0,50.05 -1313,30MAY2009:11:00:00,,51.05 -1313,30MAY2009:11:30:00,,52.05 -6300,20JUL2009:08:30:00,,500.30 -6300,20JUL2009:09:00:00,,501.31 -6300,20JUL2009:09:30:00,1.001,502.32 diff --git a/old_C_code/stage1/test/tdata/sample_byRows.bin b/old_C_code/stage1/test/tdata/sample_byRows.bin deleted file mode 100644 index 0bb324b..0000000 Binary files a/old_C_code/stage1/test/tdata/sample_byRows.bin and /dev/null differ diff --git a/old_C_code/stage1/test/tdata/sample_byRows.csv b/old_C_code/stage1/test/tdata/sample_byRows.csv deleted file mode 100644 index a91eafb..0000000 --- a/old_C_code/stage1/test/tdata/sample_byRows.csv +++ /dev/null @@ -1,4 +0,0 @@ -12301,23.051,,33.052,43.053 -50000,150.321,160.322,140.323 -1313,50.05,51.05,52.05 -6300,500.30,501.31,,502.32 diff --git a/old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2009.r b/old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2009.r deleted file mode 100644 index bf2578b..0000000 --- a/old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2009.r +++ /dev/null @@ -1,172 +0,0 @@ -## File : 00_convertir-donnnes_2009.r -## Description : Converts flat EDF's 32K data into a full data matrix -## layout [individuals, variables]. Rownames are EDF's ids. -## We process the original flat file sequentially by lines -## to avoid exceding the available RAM memory (and so avoiding -## swaping which is a computational burden). - - -rm(list = ls()) - -# setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes") - - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6000, 3), 7011) # We'll process 1000 x p lines at each - # iteration of the reading loop - -## 2. Process the large flat file #### -## We want to check that every time step recorded for each id. - -con <- file("~/tmp/data/2009.csv") # Establish a connection to the file -open(con, "r") # Open the connection -rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb * length(dates)) - auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE) - rm(actual) - auxdf <- data.frame(id = as.integer(auxmat[, 1]), - date = auxmat[, 2], - val = as.numeric( - ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))) - rm(auxmat) # free up some space - - tab <- table(auxdf$id) - idtt <- NULL - for(tt in as.integer(names(which(tab < p)))) { # id with less than p records! - print(tt) - idtt <- c(idtt, which(auxdf$id == tt)) - } - - idmat <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE) - alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0 - valmat <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE) - - # store separatelly full records from partial records - write.table(file = paste0("~/tmp/2009_full_", b, ".txt"), valmat, - row.names = idmat[, 1], col.names = FALSE) - write.table(file = paste0("~/tmp/2009_partial_", b, ".txt"), auxdf[idtt, ]) -} - -close(con) # close connection to the file - -rm(auxdf, idmat, valmat, alldatesperid, b, # clean up some memory - idtt, blocks, tab, tt, con) - - -## 3. Complete partial records #### -## After analysis, partial records are only 119 clients from which one only -## time step (01JAN2009:00:00:00) is lacking. - -df_partial <- NULL -for(f in list.files("~/tmp/", "2009_partial_*")) - df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f))) - -tab <- table(df_partial$id) -id_incomp <- as.integer(names(which(tab < p))) # Incomplete records - -df_partial_full <- rbind(df_partial, - data.frame(id = id_incomp, - date = "01JAN2009:00:00:00", - val = NA)) - -rm(df_partial) - -# tab2 <- table(df_partial_full$id) # Check that df_partial_full is full -# head(sort(tab2)) - - -## 4. Reorder the lines to get the data matrix #### -## As we paste chunks of partial records and impute some time steps, -## the original order of the data is broken. We fix it by reordering -## the ids and then the data. - -idx_ordered <- order(df_partial_full$id) # order ids -df_partial_full2 <- df_partial_full[idx_ordered, ] -rm(df_partial_full) - -# Order data values following the correct dates (as the date is a factor -# we need to seek for each value: this is computationnaly innefficient). - -valmat <- matrix(df_partial_full2$val, ncol = p, byrow = TRUE) -datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE) -idmat <- matrix(df_partial_full2$id, ncol = p, byrow = TRUE) - -# Use this for as a check by running it twice. On the second run no -# printing should be done (because records should be ordered). -for(line in 1:nrow(datemat)) { - if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered - cat(sprintf("\nline %i is not ordered", line)) - - neworder <- match(dates, datemat[line, ]) - valmat[line , ] <- valmat[ line, neworder] - datemat[line , ] <- datemat[line, neworder] - } -} - - -## 5. Write on disk the full data matrix of partial records #### - -write.table(file = "~/tmp/2009_full_Z.txt", valmat, - row.names = idmat[, 1], col.names = FALSE) -rm(list = ls()) -gc() - -## 6. Compile data files in BASH #### - -# cat 2009_full*.txt > 2009_full.txt -# rm 2009_full_*.txt 2009_partial_*.txt - - -## A. data.table & reshape2 #### -## When large RAM memory is available, one could use this code to process -## everything in memory. - -#library(data.table) -#library(reshape2) - -#dt <- fread(input = "~/tmp/data/2009_chunk.csv") - -#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), -# CPP_PUISSANCE_BRUTE, -# CPP_PUISSANCE_CORRIGEE), ] -#dt[, CPP_PUISSANCE_CORRIGEE := NULL] -#dt[, CPP_PUISSANCE_BRUTE := NULL] - -#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge) - - -## Z. Probably stuff to be deleted - -# searchpos <- function(row) { -# str <- strsplit(row, ",") -# -# auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str) -# -# auxdf <- data.frame(id = as.integer(auxmat[, 1]), -# date = auxmat[, 2], -# val = as.numeric( -# ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])) -# ) -# rm(auxmat) -# -# idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE) -# alldatesperid <- apply(idmat, 1, sd) == 0 -# -# -# # lines <- match(auxdf$id, identifiants) -# # cols <- match(auxdf$date, dates) -# -# return(cbind(lines, cols, auxdf$val)) -# } diff --git a/old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2010.r b/old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2010.r deleted file mode 100644 index 1f2326b..0000000 --- a/old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2010.r +++ /dev/null @@ -1,174 +0,0 @@ -## File : 00_convertir-donnnes_2010.r -## Description : Converts flat EDF's 32K data into a full data matrix -## layout [individuals, variables]. Rownames are EDF's ids. -## We process the original flat file sequentially by lines -## to avoid exceding the available RAM memory (and so avoiding -## swaping which is a computational burden). - - -rm(list = ls()) - -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2010", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(1000, 24), 1011) # We'll process 1000 x p lines at each - # iteration of the reading loop - -## 2. Process the large flat file #### -## We want to check that every time step recorded for each id. - -con <- file("~/tmp/data/2010.csv") # Establish a connection to the file -open(con, "r") # Open the connection -rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb * length(dates)) - auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE) - rm(actual) - auxdf <- data.frame(id = as.integer(auxmat[, 1]), - date = auxmat[, 2], - val = as.numeric( - ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))) - rm(auxmat) # free up some space - - tab <- table(auxdf$id) - idtt <- NULL - for(tt in as.integer(names(which(tab < p)))) { # id with less than p records! - print(tt) - idtt <- c(idtt, which(auxdf$id == tt)) - } - - if(is.null(idtt)) { # no incomplete records - idmat <- matrix(auxdf$id, ncol = p, byrow = TRUE) - alldatesperid <- apply(idmat, 1, sd) == 0 - valmat <- matrix(auxdf$val, ncol = p, byrow = TRUE) - } else { - idmat <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE) - alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0 - valmat <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE) - - # store separatelly partial records - write.table(file = paste0("~/tmp/2010_partial_", b, ".txt"), auxdf[idtt, ]) - } - - # store full records - write.table(file = paste0("~/tmp/2010_full_", b, ".txt"), valmat, - row.names = idmat[, 1], col.names = FALSE) -} - -close(con) # close connection to the file - -rm(auxdf, idmat, valmat, alldatesperid, b, # clean up some memory - idtt, blocks, tab, tt, con) - - -## 3. Complete partial records #### NOT NECESSARY FOR 2010 -## After analysis, partial records are only 119 clients from which one only -## time step (01JAN2009:00:00:00) is lacking. - -#df_partial <- NULL -#for(f in list.files("~/tmp/", "2009_partial_*")) -# df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f))) - -#tab <- table(df_partial$id) -#id_incomp <- as.integer(names(which(tab < p))) # Incomplete records - -#df_partial_full <- rbind(df_partial, -# data.frame(id = id_incomp, -# date = "01JAN2009:00:00:00", -# val = NA)) - -#rm(df_partial) - -# tab2 <- table(df_partial_full$id) # Check that df_partial_full is full -# head(sort(tab2)) - - -## 4. Reorder the lines to get the data matrix #### NOT NECESSARY FOR 2010 -## As we paste chunks of partial records and impute some time steps, -## the original order of the data is broken. We fix it by reordering -## the ids and then the data. - -#idx_ordered <- order(df_partial_full$id) # order ids -#df_partial_full2 <- df_partial_full[idx_ordered, ] -#rm(df_partial_full) - -# Order data values following the correct dates (as the date is a factor -# we need to seek for each value: this is computationnaly innefficient). - -#valmat <- matrix(df_partial_full2$val, ncol = p, byrow = TRUE) -#datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE) -#idmat <- matrix(df_partial_full2$id, ncol = p, byrow = TRUE) - -# Use this for as a check by running it twice. On the second run no -# printing should be done (because records should be ordered). -#for(line in 1:nrow(datemat)) { -# if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered -# cat(sprintf("\nline %i is not ordered", line)) -# -# neworder <- match(dates, datemat[line, ]) -# valmat[line , ] <- valmat[ line, neworder] -# datemat[line , ] <- datemat[line, neworder] -# } -#} - - -## 5. Write on disk the full data matrix of partial records #### - -#write.table(file = "~/tmp/2009_full_Z.txt", valmat, -# row.names = idmat[, 1], col.names = FALSE) -rm(list = ls()) -gc() - - -## A. data.table & reshape2 #### -## When large RAM memory is available, one could use this code to process -## everything in memory. - -#library(data.table) -#library(reshape2) - -#dt <- fread(input = "~/tmp/data/2009_chunk.csv") - -#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), -# CPP_PUISSANCE_BRUTE, -# CPP_PUISSANCE_CORRIGEE), ] -#dt[, CPP_PUISSANCE_CORRIGEE := NULL] -#dt[, CPP_PUISSANCE_BRUTE := NULL] - -#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge) - - -## Z. Probably stuff to be deleted - -# searchpos <- function(row) { -# str <- strsplit(row, ",") -# -# auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str) -# -# auxdf <- data.frame(id = as.integer(auxmat[, 1]), -# date = auxmat[, 2], -# val = as.numeric( -# ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])) -# ) -# rm(auxmat) -# -# idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE) -# alldatesperid <- apply(idmat, 1, sd) == 0 -# -# -# # lines <- match(auxdf$id, identifiants) -# # cols <- match(auxdf$date, dates) -# -# return(cbind(lines, cols, auxdf$val)) -# } diff --git a/old_C_code/stage2_UNFINISHED/src/01_extract-features_2009.r b/old_C_code/stage2_UNFINISHED/src/01_extract-features_2009.r deleted file mode 100644 index 99ab8c5..0000000 --- a/old_C_code/stage2_UNFINISHED/src/01_extract-features_2009.r +++ /dev/null @@ -1,58 +0,0 @@ -## File : 01_extract-features_2009.r -## Description : Using the full data matrix, we extract handy features to -## cluster. - -rm(list = ls()) - -source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - -# table( substr(dates, 11, 15) ) # Sunlight time saving produces an - # unbalanced number of time points - # per time stepa across the year - - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - auxDWT <- t(apply(datamat, 1, toDWT)) - auxcontrib <- t(apply(auxDWT, 1, contrib)) - rm(auxDWT) - - if(b == 1) { - matcontrib <- auxcontrib - } else { - matcontrib <- rbind(matcontrib, auxcontrib) - } - -} - -close(con) # close connection to the file - -write.table(matcontrib, file = "~/tmp/2009_contrib.txt") - diff --git a/old_C_code/stage2_UNFINISHED/src/01_extract-features_2010.r b/old_C_code/stage2_UNFINISHED/src/01_extract-features_2010.r deleted file mode 100644 index a1d7d49..0000000 --- a/old_C_code/stage2_UNFINISHED/src/01_extract-features_2010.r +++ /dev/null @@ -1,54 +0,0 @@ -## File : 01_extract-features_2010.r -## Description : Using the full data matrix, we extract handy features to -## cluster. - -rm(list = ls()) - -source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2010", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2010_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - auxDWT <- t(apply(datamat, 1, toDWT)) - auxcontrib <- t(apply(auxDWT, 1, contrib)) - rm(auxDWT) - - if(b == 1) { - matcontrib <- auxcontrib - } else { - matcontrib <- rbind(matcontrib, auxcontrib) - } - -} - -close(con) # close connection to the file - -write.table(matcontrib, file = "~/tmp/2010_contrib.txt") - diff --git a/old_C_code/stage2_UNFINISHED/src/02_cluster_2009.r b/old_C_code/stage2_UNFINISHED/src/02_cluster_2009.r deleted file mode 100644 index 3514ce7..0000000 --- a/old_C_code/stage2_UNFINISHED/src/02_cluster_2009.r +++ /dev/null @@ -1,89 +0,0 @@ -## File: extract-features.r - -rm(list = ls()) - -## a. Load data & libraries #### - -library(cluster) -MOJARRITA <- Sys.info()[4] == "mojarrita" - -#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -source('01_StBr.r') - -if(MOJARRITA){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") -} - - -matcontrib0 <- read.table(file = "~/tmp/2009_contrib.txt") -n <- nrow(matcontrib0) - -sdcontrib <- apply(matcontrib0, 1, sd) -lims <- quantile(sdcontrib, probs = c(.005, .995)) # obtain 1%-extreme data -is_normal <- which((sdcontrib > lims[1]) & (sdcontrib < lims[2])) - -matcontri_ext <- matcontrib0[-is_normal, ]#"" -matcontrib <- matcontrib0[is_normal, ] # wipe out aberrant data - -matcontrib <- t(apply(matcontrib, 1, function(x) x / sum(x))) -matcontrib <- t(apply(matcontrib, 1, function(p) log(p / (1 - p)) )) - - -## b. Transform data & compute CI #### -ci <- CI(matcontrib) -tdata <- ci$tdata; rownames(tdata) <- rownames(matcontrib) -selvar <- ci$selectv - -## c. Clustering ########## -K <- 200 -system.time( - -#TODO: cette partie en C - - clfit <- clara(x = tdata[, selvar], - k = K, - sampsize = 4000, - samples = 4, - rngR = TRUE) -) - -#save(clfit, file = 'clfit500.Rdata') -# save(clfit, file = 'clfit200RC.Rdata') -save(clfit, file = 'clfit200.Rdata') - -rm(ci, matcontrib0, is_normal, lims, selvar) -gc() - -## d. Analyze results ########## - -#1. Répartition du nombre d'observation par cluster -#plot(sort(table(clfit$clustering), decreasing = TRUE), -# type = 'l', ylab = 'Fréquence', xlab = 'Classe') - - -#clust <- res$clustering -# centres <- aggregate(conso, clust) -# table(clust) - - #sel_veille <- as.Date(rownames(conso)[sel - 1]) - #sel_lendemain <- as.Date(rownames(conso)[sel + 1]) - - #res_clust <- data.frame(date = rownames(conso), - #veille = weekdays(sel_veille), - #lendemain = weekdays(sel_lendemain), - # clust = clust) - - #for(k in 1:K) { - # assign(paste0("dates_clust", K), - # substr(subset(res_clust, clust == k)$date, 1, 7) ) - #} - - #dev.off() - - #save(file = paste0(dtitle, "_clust.Rdata"), - #res_clust, selvar, K, gap) -#} - -#dates_clust1 <- substr(subset(dates, clust == 1)$date, 1, 7) diff --git a/old_C_code/stage2_UNFINISHED/src/03_compute-sums-of-classes_2009.r b/old_C_code/stage2_UNFINISHED/src/03_compute-sums-of-classes_2009.r deleted file mode 100644 index 20d2d18..0000000 --- a/old_C_code/stage2_UNFINISHED/src/03_compute-sums-of-classes_2009.r +++ /dev/null @@ -1,80 +0,0 @@ -## File : 03_compute-sum-of-classes_2009.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - -# Fit of the clustering : clfit -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') -load('../res/clfit200RC.Rdata') -# table(clfit$clustering) - -names(clfit$clustering) <- sample(names(clfit$clustering))# for random classes -dfclust <- data.frame(cluster = clfit$clustering) -# write.table(dfclust, file = "../res/clfit200-random.txt") - -K <- nrow(clfit$clusinfo) -#dfclust <- head(dfclust, 50) # just for testing purpouses -synchros <- matrix(0, ncol = p, nrow = K) -rm(clfit) - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - # obtain for each line of datamat the cluster membership (if any) - clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] - -# if(length(which(rownames(datamat) == "218095"))>0) print(b); - - - #TODO: en C : lourd - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - - if(length(clustk) > 1) { - synchrosk <- colSums(datamat[which(clustfactor == k), ]) - } else { - synchrosk <- datamat[which(clustfactor == k), ] - } - synchros[k, ] <- synchros[k, ] + synchrosk - rm(synchrosk) - } - } -} - -close(con) # close connection to the file - -synchros <- data.frame(t(synchros), total = colSums(synchros)) - -# write.table(synchros, file = "~/tmp/2009_synchros200RC.txt") -# write.table(synchros, file = "~/tmp/2009_synchros200-random.txt") - diff --git a/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r b/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r deleted file mode 100644 index aa35d0c..0000000 --- a/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r +++ /dev/null @@ -1,239 +0,0 @@ -## File : 05_cluster2stepWER.r -## Description : - -rm(list = ls()) - -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") - -library(Rwave) # CWT -library(cluster) # pam -#library(flexclust) # kcca -source("aux.r") # auxiliary clustering functions - -#TODO: [plus tard] alternative à sowa (package disparu) : cwt.. -source("sowas-superseded.r") # auxiliary CWT functions - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- as.character(dates0[grep("2009", dates0)]) -rm(dates0) - -n <- length(identifiants) -p <- delta <- length(dates) - -synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RC.txt"))) -#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200-random.txt"))) - -nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing -synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) #valeurs après 1er janvier - -#moyenne pondérée pour compléter deux demi-heures manquantes -imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) -synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) - -conso <- synchros09[-201, ]; # series must be on rows -n <- nrow(conso) -delta <- ncol(conso) - -rm(synchros09, nas) - -## 2. Compute WER distance matrix #### - -## _.a CWT -- Filtering the lowest freqs (>6m) #### -nvoice <- 4 -# # noctave4 = 2^13 = 8192 half hours ~ 180 days -noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2, - tw = 0, noctave = 13) -# # 4 here represent 2^5 = 32 half-hours ~ 1 day -scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2 -lscvect4 <- length(scalevector4) -lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect - - -#(Benjamin) -#à partir de là , "conso" == courbes synchrones -n <- nrow(conso) -delta <- ncol(conso) - - -#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube] - -#TODO: une fonction qui fait lignes 59 à 91 - -#cube: -# Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, -# scalevector = scalevector4, -# lt = delta, smooth = FALSE, -# nvoice = nvoice) # observations node with CWT -# -# #matrix: -# ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) -# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) -# -# #NOTE: delta et lscvect pourraient etre gardés à part (communs) -# for(i in 1:n) -# Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) -# -# #rm(conso, Xcwt4); gc() -# -# ## _.b WER^2 distances ######## -# Xwer_dist <- matrix(0.0, n, n) -# for(i in 1:(n - 1)){ -# mat1 <- vect2mat(Xcwt2[i,]) -# for(j in (i + 1):n){ -# mat2 <- vect2mat(Xcwt2[j,]) -# num <- Mod(mat1 * Conj(mat2)) -# WX <- Mod(mat1 * Conj(mat1)) -# WY <- Mod(mat2 * Conj(mat2)) -# smsmnum <- smCWT(num, scalevector = scalevector4) -# smsmWX <- smCWT(WX, scalevector = scalevector4) -# smsmWY <- smCWT(WY, scalevector = scalevector4) -# wer2 <- sum(colSums(smsmnum)^2) / -# sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) -# Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) -# Xwer_dist[j, i] <- Xwer_dist[i, j] -# } -# } -# diag(Xwer_dist) <- numeric(n) -# -# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata") -# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata") - - - -#lignes 59 à 91 "dépliées" : -Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, - scalevector = scalevector4, - lt = delta, smooth = FALSE, - nvoice = nvoice) # observations node with CWT - - #matrix: - ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) - Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) - - #NOTE: delta et lscvect pourraient etre gardés à part (communs) - for(i in 1:n) - Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) - - #rm(conso, Xcwt4); gc() - - ## _.b WER^2 distances ######## - Xwer_dist <- matrix(0.0, n, n) - for(i in 1:(n - 1)){ - mat1 <- vect2mat(Xcwt2[i,]) - - #NOTE: vect2mat = as.matrix ?! (dans aux.R) - vect2mat <- function(vect){ - vect <- as.vector(vect) - matrix(vect[-(1:2)], delta, lscvect) - } - - for(j in (i + 1):n){ - mat2 <- vect2mat(Xcwt2[j,]) - num <- Mod(mat1 * Conj(mat2)) - WX <- Mod(mat1 * Conj(mat1)) - WY <- Mod(mat2 * Conj(mat2)) - smsmnum <- smCWT(num, scalevector = scalevector4) - smsmWX <- smCWT(WX, scalevector = scalevector4) - smsmWY <- smCWT(WY, scalevector = scalevector4) - wer2 <- sum(colSums(smsmnum)^2) / - sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) - Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) - Xwer_dist[j, i] <- Xwer_dist[i, j] - } - } - diag(Xwer_dist) <- numeric(n) - -#fonction smCWT (dans aux.R) - smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0, - nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, - lt= 24, dt= 0.5, scalevector ) - { -# noctave <- adjust.noctave(lt, dt, s0, tw, noctave) -# scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 - wsp <- Mod(CWT) - smwsp <- smooth.matrix(wsp, swabs) - smsmwsp <- smooth.time(smwsp, tw, dt, scalevector) - smsmwsp - } - - #dans sowas.R -smooth.matrix <- function(wt,swabs){ - - if (swabs != 0) - smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1))) - else - smwt <- wt - - smwt - -} -smooth.time <- function(wt,tw,dt,scalevector){ - - smwt <- wt - - if (tw != 0){ - for (i in 1:length(scalevector)){ - - twi <- as.integer(scalevector[i]*tw/dt) - smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1)) - - } - } - smwt -} - -#et filter() est dans stats:: - -#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c - - - -load("../res/2009_synchros200WER.Rdata") -#load("../res/2009_synchros200-randomWER.Rdata") - -## 3. Cluster using WER distance matrix #### - -#hc <- hclust(as.dist(Xwer_dist), method = "ward.D") -#plot(hc) -# -# #clust <- cutree(hc, 2) -# -for(K in 2:30){ - #K <- 3 - #pamfit <- pam(tdata[-201, ci$selectv], k = K) - pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) - - #table(pamfit$clustering) - - SC <- matrix(0, ncol = p, nrow = K) - - clustfactor <- pamfit$clustering -# for(k in 1:K){ -# clustk <- which(clustfactor == k) -# if(length(clustk) > 0) { -# if(length(clustk) > 1) { -# SCk <- colSums(synchros09[which(clustfactor == k), ]) -# } else { -# SCk <- synchros09[which(clustfactor == k), ] -# } -# SC[k, ] <- SC[k, ] + SCk -# rm(SCk) -# } -#} - -#write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) -#write.table(clustfactor, file = "~/tmp/clustfactor3.txt") -#write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt")) -write.table(clustfactor, file = paste0("~/tmp/clustfactor-randomWER", K, ".txt")) -} -# -# # Plots -# layout(1) -# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) -# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) -# -# -# diff --git a/old_C_code/stage2_UNFINISHED/src/06_predictions.r b/old_C_code/stage2_UNFINISHED/src/06_predictions.r deleted file mode 100644 index 6697161..0000000 --- a/old_C_code/stage2_UNFINISHED/src/06_predictions.r +++ /dev/null @@ -1,204 +0,0 @@ -## File : 06_predictions.r -## Description : Calculer les synchrones pour chaque groupe -## obtenu par le clustering. - -rm(list = ls()) - -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") - - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates09 <- as.character(dates0[grep("2009", dates0)]) -dates10 <- as.character(dates0[grep("2010", dates0)]) -dates <- c(dates09, dates10) -rm(dates0, dates09, dates10) - -n <- length(identifiants) -p <- length(dates) - -# groups for the predictor -gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") -i_start_calen <- which(rownames(gr_calen) == "2009-01-01") -i_end_calen <- which(rownames(gr_calen) == "2010-12-31") -gr <- gr_calen$gr[i_start_calen:i_end_calen] -gr <- gsub("Thu", "TWT", gr) -gr <- gsub("Wed", "TWT", gr) -gr <- gsub("Tue", "TWT", gr) - -days <- seq.Date(from = as.Date("2009/01/01"), - to = as.Date("2010/12/31"), - by = 'day') - -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) -#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) -#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) -synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200-random.txt")) -synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200-random.txt")) - - -synchros09 <- t(synchros09) # series must be on rows -synchros10 <- t(synchros10) # series must be on rows - -## 2. Imputation of missing values #### -# (done by linear interpolation) -nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing -synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) - -imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) -imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) - -synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) -synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) - - -rm(imput09, imput10, nas) -rm(gr_calen, i_start_calen, i_end_calen) - -# Set start and end timepoints for the prediction -i_00 <- which(days == "2009-01-01") -n_00 <- which(days == "2009-12-31") -n_01 <- which(days == "2010-12-31") - - -## 3. Construct predictors #### -library(kerwavfun) -synchros <- (cbind( synchros09, synchros10 ))[-201, ] - -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC') -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC') -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER') -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRANDOM') -clustfactors <- dir("~/tmp/", pattern = 'clustfactor-randomWER') - -maxK <- length(clustfactors) - 1 - -performK <- data.frame(K = rep(NA_real_, maxK), - mape = rep(NA_real_, maxK), - rmse = rep(NA_real_, maxK)) - -setwd("~/tmp/") -for(cf in seq_along(clustfactors)) { -# clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") - clustfactor <- read.table(file = clustfactors[cf]) - - K <- nrow(unique(clustfactor)) - - performK[cf, 1] <- K - - SC <- matrix(0, ncol = ncol(synchros), nrow = K) - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - if(length(clustk) > 1) { - SCk <- colSums(synchros[which(clustfactor == k), ]) - } else { - SCk <- synchros[which(clustfactor == k), ] - } - SC[k, ] <- SC[k, ] + SCk - rm(SCk) - } - } - - mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) - mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) - - - ## 2. Transform to wkdata (performs DWT) ## - - wk_Load <- wkdata(X = c(t(mat_Load)), - gr = gr[1:nrow(mat_Load)], - p = 48) - - wks <- lapply(mat_ks, function(mat_k) - wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) - - ## 3. Begin of big loop over ## - grid <- n_00:(n_01 - 1) # these are the reference days - #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) - - # output matrix of predicted days - # names of the predicted days (reference days + 1) - pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) - preds <- lapply(1:K, function(k) pred_Load) - - go <- Sys.time() - for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] - if((q %/% 50) == (q / 50)) cat(paste(" iteration: ", q, "\n")) - Q <- grid[q] - - select_Load <- select.wkdata(wk_Load, i_00:Q) - aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", - EPS = .Machine$double.eps) - pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X - - selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) - auxs <- lapply(selects, - function(k) wavkerfun(obj = k, - kerneltype = "gaussian", - EPS = .Machine$double.eps)) - for(k in 1:K) - preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X - - } - - top <- Sys.time() - #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) - - # Compute the prediction of synchrone by the sum of individuals - pred_Load_sum <- Reduce('+', preds) - - ## 4. Analisis of the predictions #### - resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 - resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 - - mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) - rmse_Load <- sqrt(rowMeans(resids_Load^2)) - - mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) - rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) - - performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) - performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) -#print(mean(mape_Load, na.rm = TRUE)) -#print(mean(mape_Load_sum, na.rm = TRUE)) - -} - - -#performKrc <- read.table("../res/perform200RC.txt") -#write.table(file = '~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/perform200-random.txt', -# performK) -#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt', -# performK) -#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200WER_3.txt', -# performK) - -#performK <- data.frame(K = performKrc$K, -# rc = performKrc$rmse, -# wer = performKwer$rmse) - -#perform <- rbind(performK, performKwer) -#perform <- perform[order(perform$K), ] - -plot(perform$K, perform$rmse, type = 'l', ylim = c(1.7,2.2), - lwd = 2, col = 2, xlab = "Nb Cluster", ylab = "MAPE (en %)") -lines(performK$K, performKrc$rmse, col = 3, lwd = 2) -abline(h = mean(performK$mape), lwd = 2) -legend("topright", c('AGG', 'DIS euclid', "DIS WER"), lwd = 2, col = 1:3) - - -## Plot article ENERGYCON -performKwer <- read.table("../res/perform200WER.txt") -performKwer <- performKwer[order(performKwer$K), ] - -pdf("~/perf_wer.pdf") -plot(performKwer$K, performKwer$rmse, type = 'l', - ylim = c(1.7,2.2), lwd = 2, col = 2, - xlab = "Nb Cluster", ylab = "MAPE (en %)") -abline(h = mean(performKwer$mape), lwd = 2) -legend("topright", c('AGG', "DIS WER"), lwd = 2, col = 1:2) -dev.off() diff --git a/old_C_code/stage2_UNFINISHED/src/unused/00_convertir-donnnes_2011.r b/old_C_code/stage2_UNFINISHED/src/unused/00_convertir-donnnes_2011.r deleted file mode 100644 index 53e8cdf..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/00_convertir-donnnes_2011.r +++ /dev/null @@ -1,176 +0,0 @@ -## File : 00_convertir-donnnes_2011.r -## Description : Converts flat EDF's 32K data into a full data matrix -## layout [individuals, variables]. Rownames are EDF's ids. -## We process the original flat file sequentially by lines -## to avoid exceding the available RAM memory (and so avoiding -## swaping which is a computational burden). - - -rm(list = ls()) - -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2011", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(1000, 8), 685) # We'll process 1000 x p lines at each - # iteration of the reading loop - -## 2. Process the large flat file #### -## We want to check that every time step recorded for each id. - -con <- file("~/tmp/data/2011.csv") # Establish a connection to the file -open(con, "r") # Open the connection -rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb * length(dates)) - auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 3, byrow = TRUE) - rm(actual) - auxdf <- data.frame(id = as.integer(auxmat[, 3]), - date = auxmat[, 1], - val = as.numeric(auxmat[, 2])) - rm(auxmat) # free up some space - - tab <- table(auxdf$id) - idtt <- NULL - for(tt in as.integer(names(which(tab < p)))) { # id with less than p records! - print(tt) - idtt <- c(idtt, which(auxdf$id == tt)) - } - - if(is.null(idtt)) { # no incomplete records - idmat <- matrix(auxdf$id, ncol = p, byrow = TRUE) - alldatesperid <- apply(idmat, 1, sd) == 0 - valmat <- matrix(auxdf$val, ncol = p, byrow = TRUE) - } else { - idmat <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE) - alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0 - valmat <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE) - - # store separatelly partial records - write.table(file = paste0("~/tmp/2011_partial_", b, ".txt"), auxdf[idtt, ]) - } - - # store full records - write.table(file = paste0("~/tmp/2011_full_", b, ".txt"), valmat, - row.names = idmat[, 1], col.names = FALSE) -} - -close(con) # close connection to the file - -rm(auxdf, idmat, valmat, alldatesperid, b, # clean up some memory - idtt, blocks, tab, tt, con) - - -## 3. Complete partial records #### - -# Missing data in 2011 is quite messy. The number of missing records -# vary from one client to another (see tab) - -df_partial <- NULL -for(f in list.files("~/tmp/", "2011_partial_*")) - df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f))) - -tab <- table(df_partial$id) -id_incomp <- as.integer(names(which(tab < p))) # Incomplete records - -# The equivalent of 2009's df_partial_full is not easy to construct - -#df_partial_full <- rbind(df_partial, -# data.frame(id = id_incomp, -# date = "01JAN2009:00:00:00", -# val = NA)) - -rm(df_partial) - -# tab2 <- table(df_partial_full$id) # Check that df_partial_full is full -# head(sort(tab2)) - - -## 4. Reorder the lines to get the data matrix #### -## As we paste chunks of partial records and impute some time steps, -## the original order of the data is broken. We fix it by reordering -## the ids and then the data. - -idx_ordered <- order(df_partial_full$id) # order ids -df_partial_full2 <- df_partial_full[idx_ordered, ] -rm(df_partial_full) - -# Order data values following the correct dates (as the date is a factor -# we need to seek for each value: this is computationnaly innefficient). - -valmat <- matrix(df_partial_full2$val, ncol = p, byrow = TRUE) -datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE) -idmat <- matrix(df_partial_full2$id, ncol = p, byrow = TRUE) - -# Use this for as a check by running it twice. On the second run no -# printing should be done (because records should be ordered). -for(line in 1:nrow(datemat)) { - if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered - cat(sprintf("\nline %i is not ordered", line)) - - neworder <- match(dates, datemat[line, ]) - valmat[line , ] <- valmat[ line, neworder] - datemat[line , ] <- datemat[line, neworder] - } -} - - -## 5. Write on disk the full data matrix of partial records #### - -write.table(file = "~/tmp/2009_full_Z.txt", valmat, - row.names = idmat[, 1], col.names = FALSE) -rm(list = ls()) -gc() - - -## A. data.table & reshape2 #### -## When large RAM memory is available, one could use this code to process -## everything in memory. - -#library(data.table) -#library(reshape2) - -#dt <- fread(input = "~/tmp/data/2009_chunk.csv") - -#dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE), -# CPP_PUISSANCE_BRUTE, -# CPP_PUISSANCE_CORRIGEE), ] -#dt[, CPP_PUISSANCE_CORRIGEE := NULL] -#dt[, CPP_PUISSANCE_BRUTE := NULL] - -#dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge) - - -## Z. Probably stuff to be deleted - -# searchpos <- function(row) { -# str <- strsplit(row, ",") -# -# auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str) -# -# auxdf <- data.frame(id = as.integer(auxmat[, 1]), -# date = auxmat[, 2], -# val = as.numeric( -# ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])) -# ) -# rm(auxmat) -# -# idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE) -# alldatesperid <- apply(idmat, 1, sd) == 0 -# -# -# # lines <- match(auxdf$id, identifiants) -# # cols <- match(auxdf$date, dates) -# -# return(cbind(lines, cols, auxdf$val)) -# } diff --git a/old_C_code/stage2_UNFINISHED/src/unused/00_createCalendar.r b/old_C_code/stage2_UNFINISHED/src/unused/00_createCalendar.r deleted file mode 100644 index caf48ce..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/00_createCalendar.r +++ /dev/null @@ -1,58 +0,0 @@ -## File: createCalendar.r -## Description: Create the calendar transitions to be used on KWF predictor -## Author: jc @ September 2015 - -## Function: irishCalendar -## Description: Create the calendar with day of the week and daytype. -## (It is the first step, then we need to obtain the transitions) -irishCalendar <- function(from, to){ - require(timeDate) - y0 <- as.character(substr(from, 1, 4)) - y1 <- as.character(substr(to, 1, 4)) - years <- y0:y1 - IRholidays <- c("NewYearsDay", - #saint patrick - "EasterMonday", - "GBMayDay", # may day - #june holiday - "GBSummerBankHoliday", # august holiday - #october holiday - "ChristmasDay" - #, st stephen day - ) - - - jf <- holiday(year = years, Holiday = IRholidays) - jf <- sort(unique(jf)) - dts <- timeSequence(from = paste(y0, '01', '01', sep = '-'), - to = paste(y1, '12', '31', sep = '-')) - wday <- dayOfWeek(dts) - wdayf <- unname(wday) - for(d in 1:length(wdayf)){ - if(d == 1) fpos <- 1 - if(dts[d] == jf[fpos]){ - wdayf[d] <- "Hol" - if(fpos < length(jf)) fpos <- fpos + 1 - } - } - - res <- data.frame(dow = wday, daytype = wdayf) - i0 <- which(rownames(res) == from) - i1 <- which(rownames(res) == to) - - return(res[i0:i1, ]) -} - - -# Call irishCalendar to create the calendar. -myCal <- irishCalendar(from="2011-01-01", to= "2013-12-31") -n <- nrow(myCal) - -# Obtention of the transitions -gr <- paste(myCal$daytype[1:(n - 1)], myCal$daytype[2:n], sep = '_') -gr[gr == "Hol_Hol"] <- "Sun_Hol" -myCal$gr <- c(gr, "Sat_Sun") - -# Write output to a file -# write.table(file = "calendar_ir.txt", myCal) - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/00_plots-energycon.r b/old_C_code/stage2_UNFINISHED/src/unused/00_plots-energycon.r deleted file mode 100644 index 9f70085..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/00_plots-energycon.r +++ /dev/null @@ -1,190 +0,0 @@ -## File : 00_plots-energycon.r -## Description : Using the full data matrix, we extract handy features to -## cluster. - -rm(list = ls()) - -library(Rwave) # CWT -#library(cluster) # pam -#library(flexclust) # kcca -source("aux.r") # auxiliary clustering functions -source("sowas-superseded.r") # auxiliary CWT functions -source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/codes/") - - -## 1. Read auxiliar data files #### - -#identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -#rm(dates0) - -#n <- length(identifiants) -p <- length(dates) - -#blocks <- c(rep(6500, 3), 5511) - -# table( substr(dates, 11, 15) ) # Sunlight time saving produces an -# unbalanced number of time points -# per time stepa across the year - - -## 2. Process the large file #### - -# if(exists("con")) close(con) -# con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -# open(con, "r") # Open the connection -# -# nb <- 4 -# actual <- readLines(con = con, n = nb )[-3] -# auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) -# rm(actual) -# -# datamat <- t(apply(auxmat[, -1], 1, as.numeric)) -# rownames(datamat) <- substr(auxmat[, 1], 2, 7) -# rm(auxmat) -# -# datamat <- datamat[, 1:(48 * 14)] -# p <- ncol(datamat) -# -# auxDWT <- t(apply(datamat, 1, toDWT)) -# matcontrib <- t(apply(auxDWT, 1, contrib)) -# rm(auxDWT) -# -# close(con); rm(con) # close connection to the file - - -load("~/data/Irlande/Data_CER_clean/SME.RData") - -smp <- c(4, 79, 126) - -datamat <- t(SME[1:(48 * 7), smp]) -p <- ncol(datamat) - -auxDWT <- t(apply(datamat, 1, toDWT)) -matcontrib <- t(apply(auxDWT, 1, contrib)) -rm(auxDWT) - -matplot(t(datamat), type = "l", lty = 1, - col = 1:8, lwd = 2) - -pdf("~/courbes.pdf") -#par(mai = c(1, 1, 0.8, 0.6), mfcol = c(4, 2), cex = 2) -for(courbe in 1:ncol(SME)) { - plot(SME[1:(48 * 7), courbe], main = paste(courbe), - xlab = "", ylab = "", type = "l") -} -dev.off() - - - - -## Plots for ENERGYCON full article #### -op <- par() - -col <- c("grey", "black", "black") -## Curves -pdf('~/curves.pdf', width = 12) -par(mai = c(1.3, 1.3, 0.1, 0.1), cex = 1.2) -matplot(t(datamat), type = "l", lty = c(1, 1, 2), - lwd = 2, col = col, # c(1, 2, 4), - ylab = "Load", xlab = "Time (1/2 hours)") -legend("top", c("Cust. A", "Cust. B", "Cust. C"), - col = col, lty = c(1, 1, 2), ncol = 3, lwd = 2) -# matplot(scale(t(datamat), scale = F), -# type = "l", lty = 1, col = c(1, 2, 4), lwd = 2, -# ylab = "Load", xlab = "Time (1/2 hours)") -dev.off() - -normi <- function(x) x / max(x) -normi2 <- function(x) (x - min(x)) / (max(x) - min(x)) - -matplot(apply(datamat, 1, normi2), - type = "l", lty = 1, col = c(1, 2, 4), lwd = 2, - ylab = "Load", xlab = "Time (1/2 hours)") - - -cont_dist <- dist(scale(matcontrib)) -cont_dist <- cont_dist / max(cont_dist) - - -delta <- p -n <- nrow(datamat) - -## _.a CWT -- Filtering the lowest freqs (>6m) #### -nvoice <- 4 -# noctave4 = 2^13 = 8192 half hours ~ 180 days -noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2, - tw = 0, noctave = 10) -# 10 here represent 2^10 = 1024 half-hours ~ 2 weeks day -scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2 -lscvect4 <- length(scalevector4) -lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect -Xcwt4 <- toCWT(datamat, noctave = noctave4, dt = 1, - scalevector = scalevector4, - lt = delta, smooth = FALSE, - nvoice = nvoice) # observations node with CWT - -Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length(c(Xcwt4[, ,1]))) - - -for(i in 1:n) - Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) - -rm(conso, Xcwt4); gc() - -lscvect <- 41 ## very very nasty: toCWT changes scalevector to 41 (instead of - ## the original length--37--) -## _.b WER^2 distances ######## -Xwer_dist <- matrix(0.0, n, n) -for(i in 1:(n - 1)){ - mat1 <- vect2mat(Xcwt2[i,]) - for(j in (i + 1):n){ - mat2 <- vect2mat(Xcwt2[j,]) - num <- Mod(mat1 * Conj(mat2)) - WX <- Mod(mat1 * Conj(mat1)) - WY <- Mod(mat2 * Conj(mat2)) - smsmnum <- smCWT(num, scalevector = scalevector4) - smsmWX <- smCWT(WX, scalevector = scalevector4) - smsmWY <- smCWT(WY, scalevector = scalevector4) - wer2 <- sum(colSums(smsmnum)^2) / - sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) - Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) - Xwer_dist[j, i] <- Xwer_dist[i, j] - } -} -diag(Xwer_dist) <- numeric(n) - - -Xwer_dist <- Xwer_dist / max(Xwer_dist) - - -pdf("~/cmdscale.pdf", width = 14) -#layout(matrix(1:2, 1, 2)) -par(mai = c(1, 1, 0.8, 0.6), mfcol = c(1, 2), cex = 2) -plot(cmdscale(cont_dist), pch = c("A", "B", "C"),#c(15, 17, 19), - main = "RC based distance", - #col = c(1, 2, 4), - xlim = c(-1, 1), ylim = c(-1, 1), - xlab = "", ylab = "", asp = 1) -abline(h= 0, lwd = 2); abline(v = 0, lwd = 2); grid(lty = 1) - -plot(cmdscale(Xwer_dist), pch = c("A", "B", "C"),#c(15, 17, 19), - main = "WER distance", - #col = c(1, 2, 4), - xlim = c(-1, 1), ylim = c(-1, 1), - xlab = "", ylab = "", asp = 1) -abline(h= 0, lwd = 2); abline(v = 0, lwd = 2); grid(lty = 1) -dev.off() - - - -## Effectives distribution -#load('../res/clfit500.Rdata') -pdf("~/distro500.pdf", width = 12) -plot(sort(table(clfit$clustering), decreasing = TRUE), - type ="s", ylab = "Effectives", xlab = "Class", lwd = 2) -abline(v = 200) -#dev.off() - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/01_StBr.r b/old_C_code/stage2_UNFINISHED/src/unused/01_StBr.r deleted file mode 100644 index 980a855..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/01_StBr.r +++ /dev/null @@ -1,57 +0,0 @@ -## File: StBr.r -## Description: screens meaningful variables and performns -## data transformation on clustering - -# rm(list = ls()) - - -## Description: Steinley & Brusco (2006) data transform to cluster -StBrtransform <- function(X){ - apply(X, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) -} - - -## Description: Clustering index (Steinley & Brusco (2006)) -CI <- function(X, B = 1000) { # B : number of boostrap replications - - n <- nrow(X) - p <- ncol(X) - - #ci <- apply(X, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) - ci <- StBrtransform(X) - - rc <- ci / min(ci) - minV <- which.min(rc) - - Xstar <- scale(X) - newRange <- apply(Xstar, 2, function(x) max(x) - min(x)) - - rmin <- newRange[minV] - - datat <- array(0.0, dim = dim(X)) - - # Reweighting X into datat - for(i in 1:p){ - v <- Xstar[, i] - temp <- rc[i] * (rmin / newRange[i])^2 - datat[, i] <- sqrt(temp) * v - } - - xboot <- matrix(rnorm(n * B), nrow = n) - #cinorm <- apply(xboot, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) - cinorm <- StBrtransform(xboot) - ci95 <- median(cinorm) - - #ciStar <- apply(datat, 2, function(x) 12 * var(x) / (max(x) - min(x))^2 ) - ciStar <- StBrtransform(datat) - selectv <- which(ciStar > ci95) - - return(list(selectv = selectv, - tdata = datat) ) -} - - - - -#test <- matrix(rnorm(200), 40, 5) -#CI(test)$selectv diff --git a/old_C_code/stage2_UNFINISHED/src/unused/01_extract-features2_2009.r b/old_C_code/stage2_UNFINISHED/src/unused/01_extract-features2_2009.r deleted file mode 100644 index 85a2b5b..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/01_extract-features2_2009.r +++ /dev/null @@ -1,67 +0,0 @@ -## File : 01_extract-features_2009.r -## Description : Using the full data matrix, we extract handy features to -## cluster. - -rm(list = ls()) - -#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -source('aux2.r') - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - -# table( substr(dates, 11, 15) ) # Sunlight time saving produces an - # unbalanced number of time points - # per time stepa across the year - - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - nas <- which(is.na(datamat)[, 1]) # some 1/1/2009 are missing - if(length(nas)>0) datamat[nas, 1] <- rowMeans(datamat[nas, 2:4]) - - imput <- datamat[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) - datamat <- cbind(datamat[, 1:4180], imput, datamat[, 4181:17518]) - - dayimp <- t(apply(datamat, 1, dayimpact)) - nightimp <- t(apply(datamat, 1, nightimpact)) - lunchimp <- t(apply(datamat, 1, lunchimpact)) - - auxfeat <- cbind(dayimp, nightimp, lunchimp) - - if(b == 1) { - matfeat <- auxfeat - } else { - matfeat <- rbind(matfeat, auxfeat) - } - -} - -close(con) # close connection to the file - -write.table(matfeat, file = "~/tmp/2009_impacts.txt") - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/02_cluster-par_2009.r b/old_C_code/stage2_UNFINISHED/src/unused/02_cluster-par_2009.r deleted file mode 100644 index 0f138c9..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/02_cluster-par_2009.r +++ /dev/null @@ -1,106 +0,0 @@ -## File: extract-features.r - -rm(list = ls()) - -## a. Load data & libraries #### - -#library(cluster) -#library(snow) -library(foreach) -library(doParallel) - -MOJARRITA <- Sys.info()[4] == "mojarrita" - -if(MOJARRITA){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - -#source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -source('01_StBr.r') - -matcontrib0 <- read.table(file = "~/tmp/2009_contrib.txt") -n <- nrow(matcontrib0) - -sdcontrib <- apply(matcontrib0, 1, sd) -lims <- quantile(sdcontrib, probs = c(.005, .995)) # obtain 1%-extreme data -is_normal <- which((sdcontrib > lims[1]) & (sdcontrib < lims[2])) - -matcontri_ext <- matcontrib0[-is_normal, ] -matcontrib <- matcontrib0[is_normal, ] # wipe out aberrant data - -matcontrib <- t(apply(matcontrib, 1, function(x) x / sum(x))) -matcontrib <- t(apply(matcontrib, 1, function(p) log(p / (1 - p)) )) - - -## b. Transform data & compute CI #### -ci <- CI(matcontrib) -tdata <- ci$tdata; rownames(tdata) <- rownames(matcontrib) -selvar <- ci$selectv - -## c. Clustering ########## - -#number of iterations -iters <- 20 - -#setup parallel backend to use 8 processors -cl <- makeCluster(20) -registerDoParallel(cl) - -clfitlist <- foreach(icount(iters)) %dopar% { - library(cluster) - K <- 200 - clara(x = tdata[, selvar], - k = K, - sampsize = 4000, - samples = 4, - rngR = TRUE) -} - -stopCluster(cl) - -#save(clfit, file = 'clfit500.Rdata') -# save(clfit, file = 'clfit200RC.Rdata') -#save(clfitlist, file = 'clfitlist200.Rdata') -#rm(ci, matcontrib0, is_normal, lims, selvar) -#gc() - - -res <- lapply(clfitlist, function(x) x$clustering) -names(res) <- 1:iters - -save(data.frame(res), file = 'res/clfitdf200.Rdata') - - -## d. Analyze results ########## - -#1. Répartition du nombre d'observation par cluster -#plot(sort(table(clfit$clustering), decreasing = TRUE), -# type = 'l', ylab = 'Fréquence', xlab = 'Classe') - - -#clust <- res$clustering -# centres <- aggregate(conso, clust) -# table(clust) - - #sel_veille <- as.Date(rownames(conso)[sel - 1]) - #sel_lendemain <- as.Date(rownames(conso)[sel + 1]) - - #res_clust <- data.frame(date = rownames(conso), - #veille = weekdays(sel_veille), - #lendemain = weekdays(sel_lendemain), - # clust = clust) - - #for(k in 1:K) { - # assign(paste0("dates_clust", K), - # substr(subset(res_clust, clust == k)$date, 1, 7) ) - #} - - #dev.off() - - #save(file = paste0(dtitle, "_clust.Rdata"), - #res_clust, selvar, K, gap) -#} - -#dates_clust1 <- substr(subset(dates, clust == 1)$date, 1, 7) diff --git a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes-par_2009.r b/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes-par_2009.r deleted file mode 100644 index 8e5e756..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes-par_2009.r +++ /dev/null @@ -1,76 +0,0 @@ -## File : 03_compute-sum-of-classes_2009.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -MOJARRITA <- Sys.info()[4] == "mojarrita" - -if(MOJARRITA){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -if(MOJARRITA) { - blocks <- c(rep(6500, 3), 5511) - } else { - blocks <- 25011 - } - -# Fit of the clustering : clfit -load('../res/clfitdf200.Rdata') # Loads res that containts - # clusterings memberships -res <- as.data.frame(res) - -lres <- length(res) -K <- 200 #nrow(clfit$clusinfo) - - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by as.numeric - # are NA strings - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - synchros <- lapply(res, - function(ll) { - aux <- matrix(0, ncol = p, nrow = K) - for(k in 1:K) { - clustk <- which(ll == k) - if(length(clustk) > 1) { - aux[k, ] <- colSums(datamat[ll == k, ]) - } else { - aux[k, ] <- datamat[ll == k, ] - } - } - aux - }) -} - -close(con) # close connection to the file - -# save(synchros, file = "~/tmp/2009synchrosdf200WER") - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM-par_2009.r b/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM-par_2009.r deleted file mode 100644 index cbd373b..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM-par_2009.r +++ /dev/null @@ -1,89 +0,0 @@ -## File : 03_compute-sum-of-classes_2009.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -MOJARRITA <- Sys.info()[4] == "mojarrita" - -if(MOJARRITA){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -if(MOJARRITA) { - blocks <- c(rep(6500, 3), 5511) - } else { - blocks <- 25011 - } - -# Fit of the clustering : clfit -load('../res/clfitdf200.Rdata') # Loads res that containts - # clusterings memberships -res <- as.data.frame(res) - -resRDN <- as.data.frame(lapply(res, sample)) -#save(file = "../res/clfitdf200RDN.Rdata", resRDN) -load("../res/clfitdf200RDN.Rdata") - -lres <- length(res) -K <- 200 #nrow(clfit$clusinfo) -rm(res) - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - actual_split <- strsplit(actual, " ") - rm(actual) - - #auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - #datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by - # as.numeric - auxlist <- lapply(actual_split, function(x) as.numeric(x[-1])) - datamat <- matrix(unlist(auxlist), ncol = p, byrow = TRUE) - rm(auxlist) - - # are NA strings - #rownames(datamat) <- substr(auxmat[, 1], 2, 7) - auxnames <- unlist(lapply(strsplit(actual, " "), "[", 1)) - rownames(datamat) <- substr(auxnames, 2, 7) - - rm(auxmat, actual_split) - - synchros <- lapply(resRDN, - function(ll) { - aux <- matrix(0, ncol = p, nrow = K) - for(k in 1:K) { - clustk <- which(ll == k) - if(length(clustk) > 1) { - aux[k, ] <- colSums(datamat[clustk, ]) - } else { - aux[k, ] <- datamat[clustk, ] - } - } - aux - }) -} - -close(con) # close connection to the file - -# save(synchros, file = "~/tmp/2009synchrosdf200RND") - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM_2009.r b/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM_2009.r deleted file mode 100644 index 016b6de..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM_2009.r +++ /dev/null @@ -1,82 +0,0 @@ -## File : 03_compute-sum-of-classes_2009.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants)- -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - -# Fit of the clustering : clfit -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') -load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata') -# table(clfit$clustering) - -dfclust <- data.frame(cluster = clfit$clustering) # real clustering -K <- nrow(clfit$clusinfo) - -#dfclust2 <- dfclust -dfclust$cluster <- sample(unlist(Map(rep, 1:200, table(dfclust)))) # random clust - - - -#dfclust <- head(dfclust, 50) # just for testing purpouses -synchros <- matrix(0, ncol = p, nrow = K) -rm(clfit) - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2009_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - # obtain for each line of datamat the cluster membership (if any) - clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] - -# if(length(which(rownames(datamat) == "218095"))>0) print(b); - - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - - if(length(clustk) > 1) { - synchrosk <- colSums(datamat[which(clustfactor == k), ]) - } else { - synchrosk <- datamat[which(clustfactor == k), ] - } - synchros[k, ] <- synchros[k, ] + synchrosk - rm(synchrosk) - } - } -} - -close(con) # close connection to the file - -synchros <- data.frame(t(synchros), total = colSums(synchros)) - -write.table(synchros, file = "~/tmp/2009_synchros200RANDOM.txt") -write.table(dfclust, file = "../res/dfclust200RANDOM.txt") -#write.table(synchros, file = "~/tmp/2009_synchros200RC.txt") - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM_2010.r b/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM_2010.r deleted file mode 100644 index 0c75bd0..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classesRANDOM_2010.r +++ /dev/null @@ -1,82 +0,0 @@ -## File : 03_compute-sum-of-classes_2010.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2009", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - -# Fit of the clustering : clfit -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') -load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata') -# table(clfit$clustering) - -dfclust <- data.frame(cluster = clfit$clustering) # real clustering -K <- nrow(clfit$clusinfo) - -#dfclust2 <- dfclust -dfclust$cluster <- sample(unlist(Map(rep, 1:200, table(dfclust)))) # random clust - - - -#dfclust <- head(dfclust, 50) # just for testing purpouses -synchros <- matrix(0, ncol = p, nrow = K) -rm(clfit) - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2010_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - # obtain for each line of datamat the cluster membership (if any) - clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] - -# if(length(which(rownames(datamat) == "218095"))>0) print(b); - - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - - if(length(clustk) > 1) { - synchrosk <- colSums(datamat[which(clustfactor == k), ]) - } else { - synchrosk <- datamat[which(clustfactor == k), ] - } - synchros[k, ] <- synchros[k, ] + synchrosk - rm(synchrosk) - } - } -} - -close(con) # close connection to the file - -synchros <- data.frame(t(synchros), total = colSums(synchros)) - -write.table(synchros, file = "~/tmp/2009_synchros200RANDOM.txt") -write.table(dfclust, file = "../res/dfclust200RANDOM.txt") -#write.table(synchros, file = "~/tmp/2009_synchros200RC.txt") - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes_2010-par.r b/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes_2010-par.r deleted file mode 100644 index 1a4c9a1..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes_2010-par.r +++ /dev/null @@ -1,79 +0,0 @@ -## File : 03_compute-sum-of-classes_2010-par.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -MOJARRITA <- Sys.info()[4] == "mojarrita" - -if(MOJARRITA){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - - - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2010", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - - -if(MOJARRITA) { - blocks <- c(rep(6500, 3), 5511) -} else { - blocks <- 25011 -} - -# Fit of the clustering : clfit -load('../res/clfitdf200.Rdata') # Loads res that containts -# clusterings memberships -res <- as.data.frame(res) - - -lres <- length(res) -K <- 200 #nrow(clfit$clusinfo) - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2010_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) # the NA introduced by as.numeric - # are NA strings - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - synchros <- lapply(res, - function(ll) { - aux <- matrix(0, ncol = p, nrow = K) - for(k in 1:K) { - clustk <- which(ll == k) - if(length(clustk) > 1) { - aux[k, ] <- colSums(datamat[ll == k, ]) - } else { - aux[k, ] <- datamat[ll == k, ] - } - } - aux - }) -} - - close(con) # close connection to the file - -# save(synchros, file = "~/tmp/2010synchrosdf200WER") - - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes_2010.r b/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes_2010.r deleted file mode 100644 index 18d23b4..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/03_compute-sums-of-classes_2010.r +++ /dev/null @@ -1,101 +0,0 @@ -## File : 03_compute-sum-of-classes_2010.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- dates0[grep("2010", dates0)] -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -blocks <- c(rep(6500, 3), 5511) - -# Fit of the clustering : clfit -#load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit500.Rdata') -load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200.Rdata') -load('~/Documents/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata') -# table(clfit$clustering) - -dfclust <- data.frame(cluster = clfit$clustering) -# read write.table(dfclust, file = "../res/clfit200RC-random.txt") for random - -K <- nrow(clfit$clusinfo) -#dfclust <- head(dfclust, 50) # just for testing purpouses -synchros <- matrix(0, ncol = p, nrow = K) -rm(clfit) - -## 2. Process the large file #### - -close(con) -con <- file("~/tmp/2010_full.txt") # Establish a connection to the file -open(con, "r") # Open the connection - -for(b in seq_along(blocks)){ # Reading loop - nb <- blocks[b] - actual <- readLines(con = con, n = nb ) - auxmat <- matrix(unlist(strsplit(actual, " ")), ncol = p + 1, byrow = TRUE) - rm(actual) - - datamat <- t(apply(auxmat[, -1], 1, as.numeric)) - rownames(datamat) <- substr(auxmat[, 1], 2, 7) - rm(auxmat) - - # obtain for each line of datamat the cluster membership (if any) - clustfactor <- dfclust$cluster[match(rownames(datamat), rownames(dfclust))] - - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - - if(length(clustk) > 1) { - synchrosk <- colSums(datamat[which(clustfactor == k), ]) - } else { - synchrosk <- datamat[which(clustfactor == k), ] - } - synchros[k, ] <- synchros[k, ] + synchrosk - rm(synchrosk) - } - - } -} - -close(con) # close connection to the file - -synchros <- data.frame(t(synchros), total = colSums(synchros)) - -# write.table(synchros, file = "~/tmp/2010_synchros200RC.txt") -# write.table(synchros, file = "~/tmp/2010_synchros200-random.txt") -# -# - - -dfclust <- read.table("clfit200muchos.txt") - -for(pepe in 1:10) { - synchros <- matrix(0, ncol = p, nrow = K) - clustfactor <- dfclust[match(rownames(datamat), rownames(tdata)), pepe] - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - if(length(clustk) > 1) { - synchrosk <- colSums(datamat[which(clustfactor == k), ]) - } else { - synchrosk <- datamat[which(clustfactor == k), ] - } - synchros[k, ] <- synchros[k, ] + synchrosk - rm(synchrosk) - } - } - - synchros <- data.frame(t(synchros), total = colSums(synchros)) - write.table(synchros, file = paste0(colnames(dfclust)[pepe], "2010.txt")) -} - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/04_predictions.r b/old_C_code/stage2_UNFINISHED/src/unused/04_predictions.r deleted file mode 100644 index 010880e..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/04_predictions.r +++ /dev/null @@ -1,159 +0,0 @@ -## File : 04_predictions.r -## Description : Calculer les synchrones pour chaque groupe obtenu par le -## clustering. - -rm(list = ls()) - -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") - -## 1. Read auxiliar data files #### - -#identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates09 <- as.character(dates0[grep("2009", dates0)]) -dates10 <- as.character(dates0[grep("2010", dates0)]) -dates <- c(dates09, dates10) -rm(dates0, dates09, dates10) - -n <- length(identifiants) -p <- length(dates) - -# groups for the predictor -gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") -i_start_calen <- which(rownames(gr_calen) == "2009-01-01") -i_end_calen <- which(rownames(gr_calen) == "2010-12-31") -gr <- gr_calen$gr[i_start_calen:i_end_calen] -gr <- gsub("Thu", "TWT", gr) -gr <- gsub("Wed", "TWT", gr) -gr <- gsub("Tue", "TWT", gr) - -days <- seq.Date(from = as.Date("2009/01/01"), - to = as.Date("2010/12/31"), - by = 'day') - -synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) -synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) - -## 2. Imputation of missing values #### -# (done by linear interpolation) -nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing -synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) - -imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) -imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) - -synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) -synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) - - -rm(imput09, imput10, nas) -rm(gr_calen, i_start_calen, i_end_calen) - -# Set start and end timepoints for the prediction -i_00 <- which(days == "2009-01-01") -n_00 <- which(days == "2009-12-31") -n_01 <- which(days == "2010-12-31") - - -## 3. Construct predictors #### -library(kerwavfun) -synchros <- (cbind( synchros09, synchros10 )) - -clustfactors <- dir("~/tmp/", pattern = 'clustfactor') -maxK <- length(clustfactors) - 1 - -performK <- data.frame(K = rep(NA_real_, maxK), - mape = rep(NA_real_, maxK), - rmse = rep(NA_real_, maxK)) - -setwd("~/tmp/") -for(cf in seq_along(clustfactors)) { - #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") - clustfactor <- read.table(file = clustfactors[cf]) - - K <- nrow(unique(clustfactor)) - - performK[cf, 1] <- K - - SC <- matrix(0, ncol = ncol(synchros), nrow = K) - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - if(length(clustk) > 1) { - SCk <- colSums(synchros[which(clustfactor == k), ]) - } else { - SCk <- synchros[which(clustfactor == k), ] - } - SC[k, ] <- SC[k, ] + SCk - rm(SCk) - } - } - - mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) - mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) - - - ## 2. Transform to wkdata (performs DWT) ## - - wk_Load <- wkdata(X = c(t(mat_Load)), - gr = gr[1:nrow(mat_Load)], - p = 48) - - wks <- lapply(mat_ks, function(mat_k) - wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) - - ## 3. Begin of big loop over ## - grid <- n_00:(n_01 - 1) # these are the reference days - #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) - - # output matrix of predicted days - # names of the predicted days (reference days + 1) - pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) - preds <- lapply(1:K, function(k) pred_Load) - - go <- Sys.time() - for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] - if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) - Q <- grid[q] - - select_Load <- select.wkdata(wk_Load, i_00:Q) - aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", - EPS = .Machine$double.eps) - pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X - - selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) - auxs <- lapply(selects, - function(k) wavkerfun(obj = k, - kerneltype = "gaussian", - EPS = .Machine$double.eps)) - for(k in 1:K) - preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X - - } - - top <- Sys.time() - #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) - - # Compute the prediction of synchrone by the sum of individuals - pred_Load_sum <- Reduce('+', preds) - - ## 4. Analisis of the predictions #### - resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 - resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 - - mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) - rmse_Load <- sqrt(rowMeans(resids_Load^2)) - - mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) - rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) - - performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) - performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) -#print(mean(mape_Load, na.rm = TRUE)) -#print(mean(mape_Load_sum, na.rm = TRUE)) - -} - -performK <- performK[order(performK$K), ] -plot(performK$K, performK$rmse, type = 'l', ylim = c(2,3)) -abline(h = mean(performK$mape), col = 2) diff --git a/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2step.r b/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2step.r deleted file mode 100644 index 70dd0df..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2step.r +++ /dev/null @@ -1,74 +0,0 @@ -## File : 05_cluster2step.r -## Description : - -rm(list = ls()) - -setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -source("~/Documents/projects/2014_EDF-Lyon2/R/01_StBr.r") # aux functs -source('http://eric.univ-lyon2.fr/~jcugliari/codes/functional-clustering.r') -library(cluster) - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- as.character(dates0[grep("2009", dates0)]) -rm(dates0) - -n <- length(identifiants) -p <- length(dates) - -synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) -nas <- which(is.na(synchros09)[1, ]) # some 1/1/2009 are missing -synchros09[nas, 1] <- colMeans(synchros09[2:4, nas]) - -#synchros09 <- t(synchros09) # series on rows, use if necessary - -## 2. Compute contributions #### -auxDWT <- t(apply(synchros09, 1, toDWT)) -matcontrib <- t(apply(auxDWT, 1, contrib, rel = TRUE, logit = TRUE)) -rm(auxDWT) - -## 3. Transform data & compute CI #### -ci <- CI(matcontrib[-201, ]) # last row has the total -tdata <- ci$tdata; rownames(tdata) <- rownames(matcontrib)[-201] - -hc <- hclust(dist(tdata[, ci$selectv]), method = "ward.D") -plot(hc) - -#clust <- cutree(hc, 2) - -for(K in 2:30){ - #K <- 3 - pamfit <- pam(tdata[-201, ci$selectv], k = K, stand = FALSE) - - #table(pamfit$clustering) - - SC <- matrix(0, ncol = p, nrow = K) - - clustfactor <- pamfit$clustering -# for(k in 1:K){ -# clustk <- which(clustfactor == k) -# if(length(clustk) > 0) { -# if(length(clustk) > 1) { -# SCk <- colSums(synchros09[which(clustfactor == k), ]) -# } else { -# SCk <- synchros09[which(clustfactor == k), ] -# } -# SC[k, ] <- SC[k, ] + SCk -# rm(SCk) -# } -#} - - write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) -#write.table(clustfactor, file = "~/tmp/clustfactor3.txt") -} - -# Plots -layout(1) -matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) -matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) - - - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-RANDOM.r b/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-RANDOM.r deleted file mode 100644 index 5ba44a1..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-RANDOM.r +++ /dev/null @@ -1,135 +0,0 @@ -## File : 05_cluster2stepWER.r -## Description : - -rm(list = ls()) - -if(Sys.info()[4] == "mojarrita"){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - -library(Rwave) # CWT -library(cluster) # pam -#library(flexclust) # kcca -source("aux.r") # auxiliary clustering functions -source("sowas-superseded.r") # auxiliary CWT functions - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- as.character(dates0[grep("2009", dates0)]) -rm(dates0) - -n <- length(identifiants) -p <- delta <- length(dates) - -load("~/tmp/2009_synchros200RND") -synchros09 <- synchros[[1]] -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt")) -#synchros09 <- t(as.matrix(read.table("~/tmp/2009_synchros200RANDOM.txt"))) -nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing -synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) - -imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) -synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) - -conso <- (synchros09)[-201, ]; # series must be on rows -n <- nrow(conso) -delta <- ncol(conso) - -rm(synchros09, nas) - -## 2. Compute WER distance matrix #### - -## _.a CWT -- Filtering the lowest freqs (>6m) #### -nvoice <- 4 -# noctave4 = 2^13 = 8192 half hours ~ 180 days -noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2, - tw = 0, noctave = 13) -# 4 here represent 2^5 = 32 half-hours ~ 1 day -scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2 -lscvect4 <- length(scalevector4) -lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect -Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, - scalevector = scalevector4, - lt = delta, smooth = FALSE, - nvoice = nvoice) # observations node with CWT - -Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) -Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) - -for(i in 1:n) - Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) - -rm(conso, Xcwt4); gc() - -## _.b WER^2 distances ######## -Xwer_dist <- matrix(0.0, n, n) -for(i in 1:(n - 1)){ - cat(sprintf('\nIter: , %i', i)) - mat1 <- vect2mat(Xcwt2[i,]) - for(j in (i + 1):n){ - mat2 <- vect2mat(Xcwt2[j,]) - num <- Mod(mat1 * Conj(mat2)) - WX <- Mod(mat1 * Conj(mat1)) - WY <- Mod(mat2 * Conj(mat2)) - smsmnum <- smCWT(num, scalevector = scalevector4) - smsmWX <- smCWT(WX, scalevector = scalevector4) - smsmWY <- smCWT(WY, scalevector = scalevector4) - wer2 <- sum(colSums(smsmnum)^2) / - sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) - Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) - Xwer_dist[j, i] <- Xwer_dist[i, j] - } -} -diag(Xwer_dist) <- numeric(n) - -save(Xwer_dist, file = "../res/2009_synchros200RANDOM-WER.Rdata") - -#load("../res/2009_synchros200WER.Rdata") - - -## 3. Cluster using WER distance matrix #### - -#hc <- hclust(as.dist(Xwer_dist), method = "ward.D") -#plot(hc) -# -# #clust <- cutree(hc, 2) -# - for(K in 2:30){ - #K <- 3 - #pamfit <- pam(tdata[-201, ci$selectv], k = K) - pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) - - #table(pamfit$clustering) - - SC <- matrix(0, ncol = p, nrow = K) - - clustfactor <- pamfit$clustering - # for(k in 1:K){ - # clustk <- which(clustfactor == k) - # if(length(clustk) > 0) { - # if(length(clustk) > 1) { - # SCk <- colSums(synchros09[which(clustfactor == k), ]) - # } else { - # SCk <- synchros09[which(clustfactor == k), ] - # } - # SC[k, ] <- SC[k, ] + SCk - # rm(SCk) - # } - #} - -# #write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) -# #write.table(clustfactor, file = "~/tmp/clustfactor3.txt") - write.table(clustfactor, file = paste0("~/tmp/clustfactorRANDOM", K, ".txt")) - } -# -# # Plots -# layout(1) -# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) -# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) -# -# -# diff --git a/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-par.r b/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-par.r deleted file mode 100644 index 90988dc..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/05_cluster2stepWER-par.r +++ /dev/null @@ -1,153 +0,0 @@ -## File : 05_cluster2stepWER.r -## Description : - -rm(list = ls()) - -if(Sys.info()[4] == "mojarrita"){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") -} - -library(Rwave) # CWT -library(cluster) # pam -#library(flexclust) # kcca -source("aux.r") # auxiliary clustering functions -source("sowas-superseded.r") # auxiliary CWT functions - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates <- as.character(dates0[grep("2009", dates0)]) -rm(dates0) - -#n <- length(identifiants) -#p <- length(dates) - -load('~/tmp/2009synchrosdf200WER') -#load('../res/2009_synchros200WER.Rdata') -synchros09 <- synchros -load('~/tmp/2010synchrosdf200WER') -synchros10 <- synchros; rm(synchros) - -conso <- lapply(synchros09, function(ll) { - nas <- which(is.na(ll)[, 1]) # some 1/1/2009 are missing - ll[nas, 1] <- rowMeans(ll[nas, 2:4]) - imput <- ll[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) - cbind(ll[, 1:4180], imput, ll[, 4181:17518]) } ) - -n <- nrow(conso[[1]]) -delta <- ncol(conso[[1]]) -rm(synchros09) - - -## 2. Compute WER distance matrix #### - -## _.a CWT -- Filtering the lowest freqs (>6m) #### -nvoice <- 4 -# noctave4 = 2^13 = 8192 half hours ~ 180 days -noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2, - tw = 0, noctave = 13) -# 4 here represent 2^5 = 32 half-hours ~ 1 day -scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2 -lscvect4 <- length(scalevector4) -lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect - -Xcwt4 <- lapply(conso, function(ll) - toCWT(ll, noctave = noctave4, dt = 1, - scalevector = scalevector4, - lt = delta, smooth = FALSE, - nvoice = nvoice) # observations node with CWT - ) - -rm(conso) - -Xcwt4 <- lapply(conso, function(ll) { - aux <- toCWT(ll, noctave = noctave4, dt = 1, - scalevector = scalevector4, - lt = delta, smooth = FALSE, - nvoice = nvoice) # observations node with CWT - res <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(aux[,,1])))) - for(i in 1:n) - res[i, ] <- c(delta, lscvect, res[,,i] / max(Mod(res[,,i])) ) - res }) - -rm(conso) - - -#Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect) -#Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1])))) - -#for(i in 1:n) -# Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) - -#rm(conso, Xcwt4); gc() - -## _.b WER^2 distances ######## -#Xwer_dist <- matrix(0.0, n, n) -#for(i in 1:(n - 1)){ -# mat1 <- vect2mat(Xcwt2[i,]) -# for(j in (i + 1):n){ -# mat2 <- vect2mat(Xcwt2[j,]) -# num <- Mod(mat1 * Conj(mat2)) -# WX <- Mod(mat1 * Conj(mat1)) -# WY <- Mod(mat2 * Conj(mat2)) -# smsmnum <- smCWT(num, scalevector = scalevector4) -# smsmWX <- smCWT(WX, scalevector = scalevector4) -# smsmWY <- smCWT(WY, scalevector = scalevector4) -# wer2 <- sum(colSums(smsmnum)^2) / -# sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) -# Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2)) -# Xwer_dist[j, i] <- Xwer_dist[i, j] -# } -# } -# diag(Xwer_dist) <- numeric(n) -# -# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata") - -load("../res/2009_synchros200WER.Rdata") - - -## 3. Cluster using WER distance matrix #### - -#hc <- hclust(as.dist(Xwer_dist), method = "ward.D") -#plot(hc) -# -# #clust <- cutree(hc, 2) -# -for(K in 2:30){ - #K <- 3 - #pamfit <- pam(tdata[-201, ci$selectv], k = K) - pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) - - #table(pamfit$clustering) - - SC <- matrix(0, ncol = p, nrow = K) - - clustfactor <- pamfit$clustering -# for(k in 1:K){ -# clustk <- which(clustfactor == k) -# if(length(clustk) > 0) { -# if(length(clustk) > 1) { -# SCk <- colSums(synchros09[which(clustfactor == k), ]) -# } else { -# SCk <- synchros09[which(clustfactor == k), ] -# } -# SC[k, ] <- SC[k, ] + SCk -# rm(SCk) -# } -#} - -#write.table(clustfactor, file = paste0("~/tmp/clustfactorRC", K, ".txt")) -#write.table(clustfactor, file = "~/tmp/clustfactor3.txt") -write.table(clustfactor, file = paste0("~/tmp/clustfactorWER", K, ".txt")) -} -# -# # Plots -# layout(1) -# matplot(t(SC)[48*10 + 1:(48*30), ], type = 'l', ylab = '',col = 1:3, lty = 1) -# matplot(t(SC)[48*100 + 1:(48*30), ], type = 'l', ylab = '', col = 1:3, lty = 1) -# -# -# diff --git a/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-ICAME.r b/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-ICAME.r deleted file mode 100644 index b7696a2..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-ICAME.r +++ /dev/null @@ -1,160 +0,0 @@ -## File : 06_predictions-ICAME.r -## Description : Prévoir les synchrones des classes ICAME. - - -rm(list = ls()) - -if(Sys.info()[4] == "mojarrita"){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - -## 1. Read auxiliar data files #### -#identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates09 <- as.character(dates0[grep("2009", dates0)]) -dates10 <- as.character(dates0[grep("2010", dates0)]) -dates <- c(dates09, dates10) -rm(dates0, dates09, dates10) - -#n <- length(identifiants) -p <- length(dates) - -# groups for the predictor -gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") -i_start_calen <- which(rownames(gr_calen) == "2009-01-01") -i_end_calen <- which(rownames(gr_calen) == "2010-12-31") -gr <- gr_calen$gr[i_start_calen:i_end_calen] -gr <- gsub("Thu", "TWT", gr) -gr <- gsub("Wed", "TWT", gr) -gr <- gsub("Tue", "TWT", gr) - -days <- seq.Date(from = as.Date("2009/01/01"), - to = as.Date("2010/12/31"), - by = 'day') - - -setwd("../classes ICAME/synchrones_classif_ICAME/") -files_ICAME <- dir(pattern = "*TR-32V*") -K_ICAME <- length(files_ICAME) -SC <- matrix(NA_real_, nrow = K_ICAME, ncol = p + 4) # NA missing in dates - # (summer time saving) - -for(k in 1:K_ICAME){ - df <- read.table(file = files_ICAME[k], header = TRUE) - SC[k, ] <- subset(x = df, # series must be on rows - subset = Year == "2009" | Year == "2010", - select = "Load")[[1]] -} - - -rm(df, files_ICAME, k) -rm(gr_calen, i_start_calen, i_end_calen) - -# Set start and end timepoints for the prediction -i_00 <- which(days == "2009-01-01") -n_00 <- which(days == "2009-12-31") -n_01 <- which(days == "2010-12-31") - - -## 2. Transform data into wkdata #### -library(kerwavfun) - -mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) -mat_ks <- lapply(1:K_ICAME, function(k) matrix(data = SC[k, ], - ncol = 48, - byrow = TRUE)) - -wk_Load <- wkdata(X = c(t(mat_Load)), - gr = gr[1:nrow(mat_Load)], - p = 48) - -wks <- lapply(mat_ks, function(mat_k) - wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) - -## 3. Begin of big loop over grid ## -grid <- n_00:(n_01 - 1) # these are the reference days -#h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) - -# output matrix of predicted days -# names of the predicted days (reference days + 1) -pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) -preds <- lapply(1:K_ICAME, function(k) pred_Load) - -go <- Sys.time() -for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] - if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) - Q <- grid[q] - - select_Load <- select.wkdata(wk_Load, i_00:Q) - aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", - EPS = .Machine$double.eps) - pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X - - selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) - auxs <- lapply(selects, - function(k) wavkerfun(obj = k, - kerneltype = "gaussian", - EPS = .Machine$double.eps)) - for(k in 1:K_ICAME) - preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X - -} -top <- Sys.time() -#rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) - - -preds <- lapply(preds, function(dd) {dd[dd<0] <- 0; dd} ) -# Compute the prediction of synchrone by the sum of individuals -pred_Load_sum <- Reduce('+', preds) - - -vect_preds <- data.frame(lapply(preds, function(dd) c(t(dd)))) -names(vect_preds) <- paste0("icame", 1:K_ICAME) - -vect_Load <- c(t(pred_Load)) -vect_gtop <- matrix(NA_real_, ncol = K_ICAME, nrow = length(vect_Load)) - - -for(tt in seq_along(vect_gtop)) { - VALID <- !any(is.na(vect_preds[tt, ])) & # compute gtop only when no NAs - tt < 17511 # these rows present a problem ==> why? (matbe too much 0s in preds_indiv) - if(VALID) { - res <- gtop(preds_indiv = as.numeric(vect_preds[tt, ]), - pred_total = vect_Load[tt], - weights_indiv = rep(1, K_ICAME), - weight_total = 1, - bounds_indiv = 0.05 * as.numeric(vect_preds[tt, ])) - vect_gtop[tt, ] <- res$preds_indiv[1:K_ICAME] # last value is the sum - } - } - -vect_gtop_sum <- rowSums(vect_gtop) -pred_gtop_sum <- matrix(vect_gtop_sum, ncol = 48, byrow = TRUE) - -## 4. Analisis of the predictions #### -resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 -resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 -resids_gtop_sum <- pred_gtop_sum - mat_Load[grid + 1, ] - -mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) -rmse_Load <- sqrt(rowMeans(resids_Load^2)) - -mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) -rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) - -mape_gtop_sum <- 100 * rowMeans(abs(resids_gtop_sum / mat_Load[grid + 1, ])) -rmse_gtop_sum <- sqrt(rowMeans(resids_gtop_sum^2)) - - -mean(mape_Load, na.rm = TRUE) -mean(mape_Load_sum, na.rm = TRUE) -mean(mape_gtop_sum, na.rm = TRUE) -#print(mean(mape_Load, na.rm = TRUE)) -#print(mean(mape_Load_sum, na.rm = TRUE)) - - -mean(rmse_Load, na.rm = TRUE) -mean(rmse_Load_sum, na.rm = TRUE) -mean(rmse_gtop_sum, na.rm = TRUE) diff --git a/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-RANDOM.r b/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-RANDOM.r deleted file mode 100644 index efc315c..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-RANDOM.r +++ /dev/null @@ -1,218 +0,0 @@ -## File : 06_predictions.r -## Description : Calculer les synchrones pour chaque groupe -## obtenu par le clustering. - -rm(list = ls()) - -if(Sys.info()[4] == "mojarrita"){ - setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates09 <- as.character(dates0[grep("2009", dates0)]) -dates10 <- as.character(dates0[grep("2010", dates0)]) -dates <- c(dates09, dates10) -rm(dates0, dates09, dates10) - -n <- length(identifiants) -p <- length(dates) - -# groups for the predictor -gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") -i_start_calen <- which(rownames(gr_calen) == "2009-01-01") -i_end_calen <- which(rownames(gr_calen) == "2010-12-31") -gr <- gr_calen$gr[i_start_calen:i_end_calen] -gr <- gsub("Thu", "TWT", gr) -gr <- gsub("Wed", "TWT", gr) -gr <- gsub("Tue", "TWT", gr) - -days <- seq.Date(from = as.Date("2009/01/01"), - to = as.Date("2010/12/31"), - by = 'day') - -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) -#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) -#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) -synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) -synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) - - -synchros09 <- t(synchros09) # series must be on rows -synchros10 <- t(synchros10) # series must be on rows - -## 2. Imputation of missing values #### -# (done by linear interpolation) -nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing -synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) - -imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) -imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) - -synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) -synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) - - -rm(imput09, imput10, nas) -rm(gr_calen, i_start_calen, i_end_calen) - -# Set start and end timepoints for the prediction -i_00 <- which(days == "2009-01-01") -n_00 <- which(days == "2009-12-31") -n_01 <- which(days == "2010-12-31") - - -## 3. Construct predictors #### -library(kerwavfun) -synchros <- (cbind( synchros09, synchros10 ))[-201, ] - -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC') -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC') -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER') -clustfactors <- dir("~/tmp/", pattern = 'clustfactorRANDOM') - -maxK <- length(clustfactors) - 1 - -performK <- data.frame(K = rep(NA_real_, maxK), - mape = rep(NA_real_, maxK), - rmse = rep(NA_real_, maxK)) - -setwd("~/tmp/") -for(cf in seq_along(clustfactors)) { - #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") - clustfactor <- read.table(file = clustfactors[cf]) - - K <- nrow(unique(clustfactor)) - - performK[cf, 1] <- K - - SC <- matrix(0, ncol = ncol(synchros), nrow = K) - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - if(length(clustk) > 1) { - SCk <- colSums(synchros[which(clustfactor == k), ]) - } else { - SCk <- synchros[which(clustfactor == k), ] - } - SC[k, ] <- SC[k, ] + SCk - rm(SCk) - } - } - - mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) - mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) - - - ## 2. Transform to wkdata (performs DWT) ## - - wk_Load <- wkdata(X = c(t(mat_Load)), - gr = gr[1:nrow(mat_Load)], - p = 48) - - wks <- lapply(mat_ks, function(mat_k) - wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) - - ## 3. Begin of big loop over ## - grid <- n_00:(n_01 - 1) # these are the reference days - #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) - - # output matrix of predicted days - # names of the predicted days (reference days + 1) - pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) - preds <- lapply(1:K, function(k) pred_Load) - - go <- Sys.time() - for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] - if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) - Q <- grid[q] - - select_Load <- select.wkdata(wk_Load, i_00:Q) - aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", - EPS = .Machine$double.eps) - pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X - - selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) - auxs <- lapply(selects, - function(k) wavkerfun(obj = k, - kerneltype = "gaussian", - EPS = .Machine$double.eps)) - for(k in 1:K) - preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X - - } - - top <- Sys.time() - #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) - - # Compute the prediction of synchrone by the sum of individuals - pred_Load_sum <- Reduce('+', preds) - - ## 4. Analisis of the predictions #### - resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 - resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 - - mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) - rmse_Load <- sqrt(rowMeans(resids_Load^2)) - - mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) - rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) - - performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) - performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) -#print(mean(mape_Load, na.rm = TRUE)) -#print(mean(mape_Load_sum, na.rm = TRUE)) - -} - - -performKwer <- performK - -#performKrc <- read.table("~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt") -#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt', -# performK) -#write.table(file = '~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/perform200RANDOM.txt', -# performK) - -#performK <- data.frame(K = performKrc$K, -# rc = performKrc$rmse, -# wer = performKwer$rmse) - - -setwd("~/recherche/03_projects/2014_EDF-Orsay-Lyon2/res/") -performKWER <- read.table("perform200WER.txt") -performKRND <- read.table("perform200RANDOM.txt") -performKrnd <- read.table("perform200-random.txt") - -performKWER <- performKWER[order(performKWER$K), ] -performKRND <- performKRND[order(performKRND$K), ] -performKrnd <- performKrnd[order(performKrnd$K), ] - -perform <- data.frame(k = performKRND$K, - k_check = performKWER$K, - random1 = performKrnd$rmse, - random2 = performKRND$rmse, - wer = performKWER$rmse, - agg = performKWER$mape) - -pdf("~/perf.pdf", width = 10) -plot(perform$k, perform$wer, type = 'l', ylim = c(1.7,2.2), - lwd = 2, col = "darkgrey", xlab = "Nb Cluster", - ylab = "MAPE (in %)") -lines(perform$k, perform$random2, lty = 2, lwd = 2) -lines(perform$k, perform$random1, lty = 3, lwd = 2) -abline(h = mean(perform$agg), lwd = 2) -legend("topright", c('AGG', "Random > WER", "RC > Random", "RC > WER"), - lwd = 2, lty = c(1, 3, 2, 1), - col = c(1, 1, 1, "darkgrey"), bg = "white") -dev.off() - - - - - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-par.r b/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-par.r deleted file mode 100644 index 4df8d27..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/06_predictions-par.r +++ /dev/null @@ -1,190 +0,0 @@ -## File : 06_predictions.r -## Description : Calculer les synchrones pour chaque groupe -## obtenu par le clustering. - -rm(list = ls()) - -if(Sys.info()[4] == "mojarrita"){ - setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/") -} else { - setwd("~/2014_EDF-Orsay-Lyon2/codes/") -} - -## 1. Read auxiliar data files #### - -identifiants <- read.table("identifs.txt")[ ,1] -dates0 <- read.table("datesall.txt")[, 1] -dates09 <- as.character(dates0[grep("2009", dates0)]) -dates10 <- as.character(dates0[grep("2010", dates0)]) -dates <- c(dates09, dates10) -rm(dates0, dates09, dates10) - -n <- length(identifiants) -p <- length(dates) - -# groups for the predictor -gr_calen <- read.table("calendar_transition_groups-1996-2011.txt") -i_start_calen <- which(rownames(gr_calen) == "2009-01-01") -i_end_calen <- which(rownames(gr_calen) == "2010-12-31") -gr <- gr_calen$gr[i_start_calen:i_end_calen] -gr <- gsub("Thu", "TWT", gr) -gr <- gsub("Wed", "TWT", gr) -gr <- gsub("Tue", "TWT", gr) - -days <- seq.Date(from = as.Date("2009/01/01"), - to = as.Date("2010/12/31"), - by = 'day') - -#synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200.txt")) -#synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200.txt")) -synchros09 <- as.matrix(read.table("~/tmp/2009_synchros200RC.txt")) -synchros10 <- as.matrix(read.table("~/tmp/2010_synchros200RC.txt")) - -synchros09 <- t(synchros09) # series must be on rows -synchros10 <- t(synchros10) # series must be on rows - -## 2. Imputation of missing values #### -# (done by linear interpolation) -nas <- which(is.na(synchros09)[, 1]) # some 1/1/2009 are missing -synchros09[nas, 1] <- rowMeans(synchros09[nas, 2:4]) - -imput09 <- synchros09[, 4180:4181] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) -imput10 <- synchros10[, 4132:4133] %*% matrix(c(2/3, 1/3, 1/3, 2/3), 2) - -synchros09 <- cbind(synchros09[, 1:4180], imput09, synchros09[, 4181:17518]) -synchros10 <- cbind(synchros10[, 1:4132], imput10, synchros10[, 4133:17518]) - - -rm(imput09, imput10, nas) -rm(gr_calen, i_start_calen, i_end_calen) - -# Set start and end timepoints for the prediction -i_00 <- which(days == "2009-01-01") -n_00 <- which(days == "2009-12-31") -n_01 <- which(days == "2010-12-31") - - -## 3. Construct predictors #### -library(kerwavfun) -synchros <- (cbind( synchros09, synchros10 ))[-201, ] - -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorAC') -#clustfactors <- dir("~/tmp/", pattern = 'clustfactorRC') -clustfactors <- dir("~/tmp/", pattern = 'clustfactorWER') - -maxK <- length(clustfactors) - 1 - -performK <- data.frame(K = rep(NA_real_, maxK), - mape = rep(NA_real_, maxK), - rmse = rep(NA_real_, maxK)) - -setwd("~/tmp/") -for(cf in seq_along(clustfactors)) { - #clustfactor <- read.table(file = "~/tmp/clustfactor3.txt") - clustfactor <- read.table(file = clustfactors[cf]) - - K <- nrow(unique(clustfactor)) - - performK[cf, 1] <- K - - SC <- matrix(0, ncol = ncol(synchros), nrow = K) - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - if(length(clustk) > 1) { - SCk <- colSums(synchros[which(clustfactor == k), ]) - } else { - SCk <- synchros[which(clustfactor == k), ] - } - SC[k, ] <- SC[k, ] + SCk - rm(SCk) - } - } - - mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE) - mat_ks <- lapply(1:K, function(k) matrix(SC[k, ], ncol = 48, byrow = TRUE)) - - - ## 2. Transform to wkdata (performs DWT) ## - - wk_Load <- wkdata(X = c(t(mat_Load)), - gr = gr[1:nrow(mat_Load)], - p = 48) - - wks <- lapply(mat_ks, function(mat_k) - wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48)) - - ## 3. Begin of big loop over ## - grid <- n_00:(n_01 - 1) # these are the reference days - #h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid)) - - # output matrix of predicted days - # names of the predicted days (reference days + 1) - pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid)) - preds <- lapply(1:K, function(k) pred_Load) - - go <- Sys.time() - for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)] - if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n")) - Q <- grid[q] - - select_Load <- select.wkdata(wk_Load, i_00:Q) - aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian", - EPS = .Machine$double.eps) - pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X - - selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q)) - auxs <- lapply(selects, - function(k) wavkerfun(obj = k, - kerneltype = "gaussian", - EPS = .Machine$double.eps)) - for(k in 1:K) - preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X - - } - - top <- Sys.time() - #rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load) - - # Compute the prediction of synchrone by the sum of individuals - pred_Load_sum <- Reduce('+', preds) - - ## 4. Analisis of the predictions #### - resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1 - resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1 - - mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ])) - rmse_Load <- sqrt(rowMeans(resids_Load^2)) - - mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ])) - rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2)) - - performK[cf, 2] <- mean(mape_Load, na.rm = TRUE) - performK[cf, 3] <- mean(mape_Load_sum, na.rm = TRUE) -#print(mean(mape_Load, na.rm = TRUE)) -#print(mean(mape_Load_sum, na.rm = TRUE)) - -} - - -performKwer <- performK - -#performKrc <- read.table("~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt") -#write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200RC.txt', -# performK) -write.table(file = '~/Documents/projects/2014_EDF-Orsay-Lyon2/res/perform200WER_3.txt', - performK) - -#performK <- data.frame(K = performKrc$K, -# rc = performKrc$rmse, -# wer = performKwer$rmse) - - -performK <- performK[order(performK$K), ] - - -#plot(performK$K, performK$rc, type = 'l', ylim = c(1.7,2.2), -# lwd = 2, col = 2) -#lines(performK$K, performK$wer, col = 3, lwd = 2) -#abline(h = mean(performKrc$mape), lwd = 2) -#legend("topright", c('Sync 32K', 'Cl. RC', "Cl. WER"), lwd = 2, col = 1:3) diff --git a/old_C_code/stage2_UNFINISHED/src/unused/analysis-SME.r b/old_C_code/stage2_UNFINISHED/src/unused/analysis-SME.r deleted file mode 100644 index 89668fa..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/analysis-SME.r +++ /dev/null @@ -1,104 +0,0 @@ -## File : ireland-data.r -## Description : - -rm(list = ls()) -setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes/") -library(Rwave) # CWT -library(cluster) # pam - -## 1. Read auxiliar data files #### -source("aux.r") # auxiliary clustering functions -source("sowas-superseded.r") # auxiliary CWT functions -load("~/data/Irlande/Data_CER_clean/SME.RData") -SME <- as.matrix(SME) - -nbdays <- nrow(SME) / 48 -nb_clust <- nbdays - 365 # last year to forecast - -id_clust <- 1:(48 * nb_clust) - -## 2. Compute WER distance matrix #### -conso <- t(SME[id_clust, ]) # ts are in lines -N <- delta <- ncol(conso) # length of one ts -n <- nrow(conso) # number of ts - -# # _.a CWT -- Filtering the lowest freqs (>6m) #### -# nvoice <- 4 -# # noctave4 = 2^12 = 4096 half hours ~ 90 days -# noctave4 <- adjust.noctave(N = N, -# dt = 1, s0 = 2, -# tw = 0, noctave = 12) -# # 4 here represent 2^5 = 32 half-hours ~ 1 day -# scalevector4 <- 2^(4:(noctave4 * nvoice) / nvoice) * 2 -# lscvect4 <- length(scalevector4) -# lscvect <- lscvect4 # i should clean my code: werFam demands a lscvect -# Xcwt4 <- toCWT(conso, noctave = noctave4, dt = 1, -# scalevector = scalevector4, -# lt = N, smooth = FALSE, -# nvoice = nvoice) # observations node with CWT -# -# -# Xcwt2 <- matrix(NA_complex_, nrow = n, ncol= 2 + length((c(Xcwt4[,,1])))) -# -# for(i in 1:n) -# Xcwt2[i,] <- c(N, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) -# -# rm(conso, Xcwt4); gc() -# -# # _.b WER^2 distances ######## -# Xwer_dist <- matrix(0.0, n, n) -# for(i in 1:(n - 1)){ -# mat1 <- vect2mat(Xcwt2[i,]) -# for(j in (i + 1):n){ -# mat2 <- vect2mat(Xcwt2[j,]) -# num <- Mod(mat1 * Conj(mat2)) -# WX <- Mod(mat1 * Conj(mat1)) -# WY <- Mod(mat2 * Conj(mat2)) -# smsmnum <- smCWT(num, scalevector = scalevector4) -# smsmWX <- smCWT(WX, scalevector = scalevector4) -# smsmWY <- smCWT(WY, scalevector = scalevector4) -# wer2 <- sum(colSums(smsmnum)^2) / -# sum( sum(colSums(smsmWX) * colSums(smsmWY)) ) -# Xwer_dist[i, j] <- sqrt(N * lscvect * (1 - wer2)) -# Xwer_dist[j, i] <- Xwer_dist[i, j] -# } -# } -# diag(Xwer_dist) <- numeric(n) -# -# save(Xwer_dist, file = "~/werdist-irlanda.Rdata") - -load("~/werdist-irlanda.Rdata") -hc <- hclust(as.dist(Xwer_dist)) - -plot(hc) - - -Ks <- c(2:10, 15, 20, 25, 30) -for(k in seq_along(Ks)){ - K <- Ks[k] - pamfit <- pam(as.dist(Xwer_dist), k = K, diss = TRUE) - - fname <- paste0("clustfactor", K) - write.table(pamfit$clustering, - file = paste0("~/tmp/clustfactorSME-", K, ".txt")) -} - - -#dir(path = '~/tmp/', pattern = 'clustfac- -K <- 2 -fname <- paste0("~/tmp/clustfactorSME-", K, ".txt") -clustfactor <- read.table(fname)$x # pamfit$clustering - for(k in 1:K){ - clustk <- which(clustfactor == k) - if(length(clustk) > 0) { - if(length(clustk) > 1) { - SCk <- colSums(SME[, which(clustfactor == k)]) - } else { - SCk <- synchros09[which(clustfactor == k), ] - } - SC[k, ] <- SC[k, ] + SCk - rm(SCk) - } - } - - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/aux.r b/old_C_code/stage2_UNFINISHED/src/unused/aux.r deleted file mode 100644 index 15021c0..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/aux.r +++ /dev/null @@ -1,127 +0,0 @@ -#################################################################### -## -## File: aux.r -## -## Description: Miscelaneous functions for clustering with kcca -## -## Modified: june 2010 -## -#################################################################### - - - ####################################################### - - # Transforms a matrix of data (one observation by row) - # into an array where position[ , , i] gives - # the smoothed modulus of the i-th cwt observation - - ######################################################## - - -##NOTE: renvoie une matrice 3D - toCWT <- function(X, sw= 0, tw= 0, swabs= 0, - nvoice= 12, noctave= 5, - s0= 2, w0= 2*pi, lt= 24, dt= 0.5, - spectra = FALSE, smooth = TRUE, - scaled = FALSE, - scalevector) - { noctave <- adjust.noctave(lt, dt, s0, tw, noctave) - if(missing(scalevector)) - scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 - res <- lapply(1:nrow(X), function(n) - { tsX <- ts( X[n,] ) - tsCent <- tsX - mean(tsX) - if(scaled) tsCent <- ts(scale(tsCent)) - tsCent.cwt <- cwt.ts(tsCent, s0, noctave, nvoice, w0) - tsCent.cwt - } ) - if( spectra ) res <- lapply(res, function(l) Mod(l)^2 ) - if( smooth ) res <- lapply(res, smCWT, swabs = swabs, - tw = tw, dt = dt, - scalevector = scalevector) - resArray <- array(NA, c(nrow(res[[1]]), ncol(res[[1]]), - length(res))) - for( l in 1:length(res) ) resArray[ , , l] <- res[[l]] - resArray - } - - - # =============================================================== - - smCWT <- function(CWT, sw= 0, tw= 0, swabs= 0, - nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, - lt= 24, dt= 0.5, scalevector ) - { -# noctave <- adjust.noctave(lt, dt, s0, tw, noctave) -# scalevector <- 2^(0:(noctave * nvoice) / nvoice) * s0 - wsp <- Mod(CWT) - smwsp <- smooth.matrix(wsp, swabs) - smsmwsp <- smooth.time(smwsp, tw, dt, scalevector) - smsmwsp - } - - - # =============================================================== - - toDWT <- function(x, filter.number = 6, family = "DaubLeAsymm") -{ x2 <- spline(x, n = 2^ceiling( log(length(x), 2) ), - method = 'natural')$y - Dx2 <- wd(x2, family = family, filter.number = filter.number)$D - Dx2 -} - - # =============================================================== - - contrib <- function(x) - { J <- log( length(x)+1, 2) - nrj <- numeric(J) - t0 <- 1 - t1 <- 0 - for( j in 1:J ) { - t1 <- t1 + 2^(J-j) - nrj[j] <- sqrt( sum( x[t0:t1]^2 ) ) - t0 <- t1 + 1 - } - return(nrj) - } - - - # ========================================= distance for coh === - - coherence <- function( x, y) - { J <- log(length(x) + 1, 2) - t0 <- 1 - sg2_x <- 0 - sg2_y <- 0 - sg_xy <- 0 - for(j in 0:(J - 1)) - { t1 <- t0 + 2^(J - j)/2 - 1 - tt <- t0:t1 - sg2_x <- sg2_x + mean(x[t0:t1]^2) - sg2_y <- sg2_y + mean(y[t0:t1]^2) - sg_xy <- sg_xy + mean(x[t0:t1] * y[t0:t1]) - t0 <- t1 + 1 - } - res <- sg_xy^2 / sg2_x / sg2_y - res - } - - - vect2mat <- function(vect){ - vect <- as.vector(vect) - matrix(vect[-(1:2)], delta, lscvect) - } - - - # ========================================= # myimg for graphics - jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", - "cyan", "#7FFF7F", "yellow", - "#FF7F00", "red", "#7F0000")) - - myimg <- function(MAT, x = 1:nrow(MAT), y = 1:col(MAT), ... ) - filled.contour( x = x, y = y, z = MAT, - xlab= 'Time', ylab= 'scale', - color.palette = jet.colors, - ... ) - - diff --git a/old_C_code/stage2_UNFINISHED/src/unused/aux2.r b/old_C_code/stage2_UNFINISHED/src/unused/aux2.r deleted file mode 100644 index d7bdb79..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/aux2.r +++ /dev/null @@ -1,25 +0,0 @@ -# File: aux2.r - -dayimpact <- function(vec) { - num <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) - den <- tapply(vec, rep(1:365, each = 48), max, na.rm = TRUE) - return(num / den) -} - -nightimpact <- function(vec) { - idnuits <- logical(48) - idnuits[c(1:14, 47:48)] <- TRUE - nuits <- vec[rep(idnuits, 365)] - num <- tapply(nuits, rep(1:365, each = sum(idnuits)), mean, na.rm = TRUE) - den <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) - return(num / den / 3) -} - -lunchimpact <- function(vec) { - idlunchs <- logical(48) - idlunchs[25:30] <- TRUE - lunchs <- vec[rep(idlunchs, 365)] - num <- tapply(lunchs, rep(1:365, each = sum(idlunchs)), mean, na.rm = TRUE) - den <- tapply(vec, rep(1:365, each = 48), mean, na.rm = TRUE) - return(num / den / 8) -} diff --git a/old_C_code/stage2_UNFINISHED/src/unused/calendar_ir.txt b/old_C_code/stage2_UNFINISHED/src/unused/calendar_ir.txt deleted file mode 100644 index ed312aa..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/calendar_ir.txt +++ /dev/null @@ -1,533 +0,0 @@ - dow daytype gr -2011-01-01 Sat Hol Hol_Sun -2011-01-02 Sun Sun Sun_Mon -2011-01-03 Mon Mon Mon_Tue -2011-01-04 Tue Tue Tue_Wed -2011-01-05 Wed Wed Wed_Thu -2011-01-06 Thu Thu Thu_Fri -2011-01-07 Fri Fri Fri_Sat -2011-01-08 Sat Sat Sat_Sun -2011-01-09 Sun Sun Sun_Mon -2011-01-10 Mon Mon Mon_Tue -2011-01-11 Tue Tue Tue_Wed -2011-01-12 Wed Wed Wed_Thu -2011-01-13 Thu Thu Thu_Fri -2011-01-14 Fri Fri Fri_Sat -2011-01-15 Sat Sat Sat_Sun -2011-01-16 Sun Sun Sun_Mon -2011-01-17 Mon Mon Mon_Tue -2011-01-18 Tue Tue Tue_Wed -2011-01-19 Wed Wed Wed_Thu -2011-01-20 Thu Thu Thu_Fri -2011-01-21 Fri Fri Fri_Sat -2011-01-22 Sat Sat Sat_Sun -2011-01-23 Sun Sun Sun_Mon -2011-01-24 Mon Mon Mon_Tue -2011-01-25 Tue Tue Tue_Wed -2011-01-26 Wed Wed Wed_Thu -2011-01-27 Thu Thu Thu_Fri -2011-01-28 Fri Fri Fri_Sat -2011-01-29 Sat Sat Sat_Sun -2011-01-30 Sun Sun Sun_Mon -2011-01-31 Mon Mon Mon_Tue -2011-02-01 Tue Tue Tue_Wed -2011-02-02 Wed Wed Wed_Thu -2011-02-03 Thu Thu Thu_Fri -2011-02-04 Fri Fri Fri_Sat -2011-02-05 Sat Sat Sat_Sun -2011-02-06 Sun Sun Sun_Mon -2011-02-07 Mon Mon Mon_Tue -2011-02-08 Tue Tue Tue_Wed -2011-02-09 Wed Wed Wed_Thu -2011-02-10 Thu Thu Thu_Fri -2011-02-11 Fri Fri Fri_Sat -2011-02-12 Sat Sat Sat_Sun -2011-02-13 Sun Sun Sun_Mon -2011-02-14 Mon Mon Mon_Tue -2011-02-15 Tue Tue Tue_Wed -2011-02-16 Wed Wed Wed_Thu -2011-02-17 Thu Thu Thu_Fri -2011-02-18 Fri Fri Fri_Sat -2011-02-19 Sat Sat Sat_Sun -2011-02-20 Sun Sun Sun_Mon -2011-02-21 Mon Mon Mon_Tue -2011-02-22 Tue Tue Tue_Wed -2011-02-23 Wed Wed Wed_Thu -2011-02-24 Thu Thu Thu_Fri -2011-02-25 Fri Fri Fri_Sat -2011-02-26 Sat Sat Sat_Sun -2011-02-27 Sun Sun Sun_Mon -2011-02-28 Mon Mon Mon_Tue -2011-03-01 Tue Tue Tue_Wed -2011-03-02 Wed Wed Wed_Thu -2011-03-03 Thu Thu Thu_Fri -2011-03-04 Fri Fri Fri_Sat -2011-03-05 Sat Sat Sat_Sun -2011-03-06 Sun Sun Sun_Mon -2011-03-07 Mon Mon Mon_Tue -2011-03-08 Tue Tue Tue_Wed -2011-03-09 Wed Wed Wed_Thu -2011-03-10 Thu Thu Thu_Fri -2011-03-11 Fri Fri Fri_Sat -2011-03-12 Sat Sat Sat_Sun -2011-03-13 Sun Sun Sun_Mon -2011-03-14 Mon Mon Mon_Tue -2011-03-15 Tue Tue Tue_Wed -2011-03-16 Wed Wed Wed_Thu -2011-03-17 Thu Hol Hol_Fri -2011-03-18 Fri Fri Fri_Sat -2011-03-19 Sat Sat Sat_Sun -2011-03-20 Sun Sun Sun_Mon -2011-03-21 Mon Mon Mon_Tue -2011-03-22 Tue Tue Tue_Wed -2011-03-23 Wed Wed Wed_Thu -2011-03-24 Thu Thu Thu_Fri -2011-03-25 Fri Fri Fri_Sat -2011-03-26 Sat Sat Sat_Sun -2011-03-27 Sun Sun Sun_Mon -2011-03-28 Mon Mon Mon_Tue -2011-03-29 Tue Tue Tue_Wed -2011-03-30 Wed Wed Wed_Thu -2011-03-31 Thu Thu Thu_Fri -2011-04-01 Fri Fri Fri_Sat -2011-04-02 Sat Sat Sat_Sun -2011-04-03 Sun Sun Sun_Mon -2011-04-04 Mon Mon Mon_Tue -2011-04-05 Tue Tue Tue_Wed -2011-04-06 Wed Wed Wed_Thu -2011-04-07 Thu Thu Thu_Fri -2011-04-08 Fri Fri Fri_Sat -2011-04-09 Sat Sat Sat_Sun -2011-04-10 Sun Sun Sun_Mon -2011-04-11 Mon Mon Mon_Tue -2011-04-12 Tue Tue Tue_Wed -2011-04-13 Wed Wed Wed_Thu -2011-04-14 Thu Thu Thu_Fri -2011-04-15 Fri Fri Fri_Sat -2011-04-16 Sat Sat Sat_Sun -2011-04-17 Sun Sun Sun_Mon -2011-04-18 Mon Mon Mon_Tue -2011-04-19 Tue Tue Tue_Wed -2011-04-20 Wed Wed Wed_Thu -2011-04-21 Thu Thu Thu_Fri -2011-04-22 Fri Fri Fri_Sat -2011-04-23 Sat Sat Sat_Sun -2011-04-24 Sun Sun Sun_Hol -2011-04-25 Mon Hol Hol_Tue -2011-04-26 Tue Tue Tue_Wed -2011-04-27 Wed Wed Wed_Thu -2011-04-28 Thu Thu Thu_Fri -2011-04-29 Fri Fri Fri_Sat -2011-04-30 Sat Sat Sat_Sun -2011-05-01 Sun Sun Sun_Hol -2011-05-02 Mon Hol Hol_Tue -2011-05-03 Tue Tue Tue_Wed -2011-05-04 Wed Wed Wed_Thu -2011-05-05 Thu Thu Thu_Fri -2011-05-06 Fri Fri Fri_Sat -2011-05-07 Sat Sat Sat_Sun -2011-05-08 Sun Sun Sun_Mon -2011-05-09 Mon Mon Mon_Tue -2011-05-10 Tue Tue Tue_Wed -2011-05-11 Wed Wed Wed_Thu -2011-05-12 Thu Thu Thu_Fri -2011-05-13 Fri Fri Fri_Sat -2011-05-14 Sat Sat Sat_Sun -2011-05-15 Sun Sun Sun_Mon -2011-05-16 Mon Mon Mon_Tue -2011-05-17 Tue Tue Tue_Wed -2011-05-18 Wed Wed Wed_Thu -2011-05-19 Thu Thu Thu_Fri -2011-05-20 Fri Fri Fri_Sat -2011-05-21 Sat Sat Sat_Sun -2011-05-22 Sun Sun Sun_Mon -2011-05-23 Mon Mon Mon_Tue -2011-05-24 Tue Tue Tue_Wed -2011-05-25 Wed Wed Wed_Thu -2011-05-26 Thu Thu Thu_Fri -2011-05-27 Fri Fri Fri_Sat -2011-05-28 Sat Sat Sat_Sun -2011-05-29 Sun Sun Sun_Mon -2011-05-30 Mon Mon Mon_Tue -2011-05-31 Tue Tue Tue_Wed -2011-06-01 Wed Wed Wed_Thu -2011-06-02 Thu Thu Thu_Fri -2011-06-03 Fri Fri Fri_Sat -2011-06-04 Sat Sat Sat_Sun -2011-06-05 Sun Sun Sun_Mon -2011-06-06 Mon Hol Hol_Tue -2011-06-07 Tue Tue Tue_Wed -2011-06-08 Wed Wed Wed_Thu -2011-06-09 Thu Thu Thu_Fri -2011-06-10 Fri Fri Fri_Sat -2011-06-11 Sat Sat Sat_Sun -2011-06-12 Sun Sun Sun_Mon -2011-06-13 Mon Mon Mon_Tue -2011-06-14 Tue Tue Tue_Wed -2011-06-15 Wed Wed Wed_Thu -2011-06-16 Thu Thu Thu_Fri -2011-06-17 Fri Fri Fri_Sat -2011-06-18 Sat Sat Sat_Sun -2011-06-19 Sun Sun Sun_Mon -2011-06-20 Mon Mon Mon_Tue -2011-06-21 Tue Tue Tue_Wed -2011-06-22 Wed Wed Wed_Thu -2011-06-23 Thu Thu Thu_Fri -2011-06-24 Fri Fri Fri_Sat -2011-06-25 Sat Sat Sat_Sun -2011-06-26 Sun Sun Sun_Mon -2011-06-27 Mon Mon Mon_Tue -2011-06-28 Tue Tue Tue_Wed -2011-06-29 Wed Wed Wed_Thu -2011-06-30 Thu Thu Thu_Fri -2011-07-01 Fri Fri Fri_Sat -2011-07-02 Sat Sat Sat_Sun -2011-07-03 Sun Sun Sun_Mon -2011-07-04 Mon Mon Mon_Tue -2011-07-05 Tue Tue Tue_Wed -2011-07-06 Wed Wed Wed_Thu -2011-07-07 Thu Thu Thu_Fri -2011-07-08 Fri Fri Fri_Sat -2011-07-09 Sat Sat Sat_Sun -2011-07-10 Sun Sun Sun_Mon -2011-07-11 Mon Mon Mon_Tue -2011-07-12 Tue Tue Tue_Wed -2011-07-13 Wed Wed Wed_Thu -2011-07-14 Thu Thu Thu_Fri -2011-07-15 Fri Fri Fri_Sat -2011-07-16 Sat Sat Sat_Sun -2011-07-17 Sun Sun Sun_Mon -2011-07-18 Mon Mon Mon_Tue -2011-07-19 Tue Tue Tue_Wed -2011-07-20 Wed Wed Wed_Thu -2011-07-21 Thu Thu Thu_Fri -2011-07-22 Fri Fri 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Wed Wed Wed_Thu -2012-03-29 Thu Thu Thu_Fri -2012-03-30 Fri Fri Fri_Sat -2012-03-31 Sat Sat Sat_Sun -2012-04-01 Sun Sun Sun_Mon -2012-04-02 Mon Mon Mon_Tue -2012-04-03 Tue Tue Tue_Wed -2012-04-04 Wed Wed Wed_Thu -2012-04-05 Thu Thu Thu_Fri -2012-04-06 Fri Fri Fri_Sat -2012-04-07 Sat Sat Sat_Sun -2012-04-08 Sun Sun Sun_Hol -2012-04-09 Mon Hol Hol_Tue -2012-04-10 Tue Tue Tue_Wed -2012-04-11 Wed Wed Wed_Thu -2012-04-12 Thu Thu Thu_Fri -2012-04-13 Fri Fri Fri_Sat -2012-04-14 Sat Sat Sat_Sun -2012-04-15 Sun Sun Sun_Mon -2012-04-16 Mon Mon Mon_Tue -2012-04-17 Tue Tue Tue_Wed -2012-04-18 Wed Wed Wed_Thu -2012-04-19 Thu Thu Thu_Fri -2012-04-20 Fri Fri Fri_Sat -2012-04-21 Sat Sat Sat_Sun -2012-04-22 Sun Sun Sun_Mon -2012-04-23 Mon Mon Mon_Tue -2012-04-24 Tue Tue Tue_Wed -2012-04-25 Wed Wed Wed_Thu -2012-04-26 Thu Thu Thu_Fri -2012-04-27 Fri Fri Fri_Sat -2012-04-28 Sat Sat Sat_Sun -2012-04-29 Sun Sun Sun_Mon -2012-04-30 Mon Mon Mon_Tue -2012-05-01 Tue Tue Tue_Wed -2012-05-02 Wed Wed Wed_Thu -2012-05-03 Thu Thu Thu_Fri -2012-05-04 Fri Fri Fri_Sat -2012-05-05 Sat Sat Sat_Sun -2012-05-06 Sun Sun Sun_Hol -2012-05-07 Mon Hol Hol_Tue -2012-05-08 Tue Tue Tue_Wed -2012-05-09 Wed Wed Wed_Thu -2012-05-10 Thu Thu Thu_Fri -2012-05-11 Fri Fri Fri_Sat -2012-05-12 Sat Sat Sat_Sun -2012-05-13 Sun Sun Sun_Mon -2012-05-14 Mon Mon Mon_Tue -2012-05-15 Tue Tue Tue_Wed -2012-05-16 Wed Wed Wed_Thu -2012-05-17 Thu Thu Thu_Fri -2012-05-18 Fri Fri Fri_Sat -2012-05-19 Sat Sat Sat_Sun -2012-05-20 Sun Sun Sun_Mon -2012-05-21 Mon Mon Mon_Tue -2012-05-22 Tue Tue Tue_Wed -2012-05-23 Wed Wed Wed_Thu -2012-05-24 Thu Thu Thu_Fri -2012-05-25 Fri Fri Fri_Sat -2012-05-26 Sat Sat Sat_Sun -2012-05-27 Sun Sun Sun_Mon -2012-05-28 Mon Mon Mon_Tue -2012-05-29 Tue Tue Tue_Wed -2012-05-30 Wed Wed Wed_Thu -2012-05-31 Thu Thu Thu_Fri -2012-06-01 Fri Fri Fri_Sat -2012-06-02 Sat Sat Sat_Sun -2012-06-03 Sun Sun Sun_Mon -2012-06-04 Mon Hol Hol_Tue -2012-06-05 Tue Tue Tue_Wed -2012-06-06 Wed Wed Wed_Thu -2012-06-07 Thu Thu Thu_Fri -2012-06-08 Fri Fri Fri_Sat -2012-06-09 Sat Sat Sat_Sun -2012-06-10 Sun Sun Sun_Mon -2012-06-11 Mon Mon Mon_Tue -2012-06-12 Tue Tue Tue_Wed -2012-06-13 Wed Wed Wed_Thu -2012-06-14 Thu Thu Thu_Fri -2012-06-15 Fri Fri Fri_Sat diff --git a/old_C_code/stage2_UNFINISHED/src/unused/calendar_transition_groups-1996-2011.txt b/old_C_code/stage2_UNFINISHED/src/unused/calendar_transition_groups-1996-2011.txt deleted file mode 100644 index 5d118ba..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/calendar_transition_groups-1996-2011.txt +++ /dev/null @@ -1,5845 +0,0 @@ -"dow" "daytype" "gr" -"1996-01-01" "Mon" "Hol" "Hol_Tue" -"1996-01-02" "Tue" "Tue" "Tue_Wed" -"1996-01-03" "Wed" "Wed" "Wed_Thu" -"1996-01-04" "Thu" "Thu" "Thu_Fri" -"1996-01-05" "Fri" "Fri" "Fri_Sat" -"1996-01-06" "Sat" "Sat" "Sat_Sun" -"1996-01-07" "Sun" "Sun" "Sun_Mon" -"1996-01-08" "Mon" "Mon" "Mon_Tue" -"1996-01-09" "Tue" "Tue" "Tue_Wed" -"1996-01-10" "Wed" "Wed" "Wed_Thu" -"1996-01-11" "Thu" "Thu" "Thu_Fri" 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"Thu_Fri" -"1996-02-09" "Fri" "Fri" "Fri_Sat" -"1996-02-10" "Sat" "Sat" "Sat_Sun" -"1996-02-11" "Sun" "Sun" "Sun_Mon" -"1996-02-12" "Mon" "Mon" "Mon_Tue" -"1996-02-13" "Tue" "Tue" "Tue_Wed" -"1996-02-14" "Wed" "Wed" "Wed_Thu" -"1996-02-15" "Thu" "Thu" "Thu_Fri" -"1996-02-16" "Fri" "Fri" "Fri_Sat" -"1996-02-17" "Sat" "Sat" "Sat_Sun" -"1996-02-18" "Sun" "Sun" "Sun_Mon" -"1996-02-19" "Mon" "Mon" "Mon_Tue" -"1996-02-20" "Tue" "Tue" "Tue_Wed" -"1996-02-21" "Wed" "Wed" "Wed_Thu" -"1996-02-22" "Thu" "Thu" "Thu_Fri" -"1996-02-23" "Fri" "Fri" "Fri_Sat" -"1996-02-24" "Sat" "Sat" "Sat_Sun" -"1996-02-25" "Sun" "Sun" "Sun_Mon" -"1996-02-26" "Mon" "Mon" "Mon_Tue" -"1996-02-27" "Tue" "Tue" "Tue_Wed" -"1996-02-28" "Wed" "Wed" "Wed_Thu" -"1996-02-29" "Thu" "Thu" "Thu_Fri" -"1996-03-01" "Fri" "Fri" "Fri_Sat" -"1996-03-02" "Sat" "Sat" "Sat_Sun" -"1996-03-03" "Sun" "Sun" "Sun_Mon" -"1996-03-04" "Mon" "Mon" "Mon_Tue" -"1996-03-05" "Tue" "Tue" "Tue_Wed" -"1996-03-06" "Wed" "Wed" "Wed_Thu" -"1996-03-07" "Thu" "Thu" "Thu_Fri" -"1996-03-08" "Fri" "Fri" "Fri_Sat" -"1996-03-09" "Sat" "Sat" "Sat_Sun" -"1996-03-10" "Sun" "Sun" "Sun_Mon" -"1996-03-11" "Mon" "Mon" "Mon_Tue" -"1996-03-12" "Tue" "Tue" "Tue_Wed" -"1996-03-13" "Wed" "Wed" "Wed_Thu" -"1996-03-14" "Thu" "Thu" "Thu_Fri" -"1996-03-15" "Fri" "Fri" "Fri_Sat" -"1996-03-16" "Sat" "Sat" "Sat_Sun" -"1996-03-17" "Sun" "Sun" "Sun_Mon" -"1996-03-18" "Mon" "Mon" "Mon_Tue" -"1996-03-19" "Tue" "Tue" "Tue_Wed" -"1996-03-20" "Wed" "Wed" "Wed_Thu" -"1996-03-21" "Thu" "Thu" "Thu_Fri" -"1996-03-22" "Fri" "Fri" "Fri_Sat" -"1996-03-23" "Sat" "Sat" "Sat_Sun" -"1996-03-24" "Sun" "Sun" "Sun_Mon" -"1996-03-25" "Mon" "Mon" "Mon_Tue" -"1996-03-26" "Tue" "Tue" "Tue_Wed" -"1996-03-27" "Wed" "Wed" "Wed_Thu" -"1996-03-28" "Thu" "Thu" "Thu_Fri" -"1996-03-29" "Fri" "Fri" "Fri_Sat" -"1996-03-30" "Sat" "Sat" "Sat_Sun" -"1996-03-31" "Sun" "Sun" "Sun_Mon" -"1996-04-01" "Mon" "Mon" "Mon_Tue" -"1996-04-02" "Tue" "Tue" "Tue_Wed" -"1996-04-03" "Wed" "Wed" "Wed_Thu" -"1996-04-04" "Thu" "Thu" "Thu_Fri" -"1996-04-05" "Fri" "Fri" "Fri_Sat" -"1996-04-06" "Sat" "Sat" "Sat_Sun" -"1996-04-07" "Sun" "Sun" "Sun_Hol" -"1996-04-08" "Mon" "Hol" "Hol_Tue" -"1996-04-09" "Tue" "Tue" "Tue_Wed" -"1996-04-10" "Wed" "Wed" "Wed_Thu" -"1996-04-11" "Thu" "Thu" "Thu_Fri" -"1996-04-12" "Fri" "Fri" "Fri_Sat" -"1996-04-13" "Sat" "Sat" "Sat_Sun" -"1996-04-14" "Sun" "Sun" "Sun_Mon" -"1996-04-15" "Mon" "Mon" "Mon_Tue" -"1996-04-16" "Tue" "Tue" "Tue_Wed" -"1996-04-17" "Wed" "Wed" "Wed_Thu" -"1996-04-18" "Thu" "Thu" "Thu_Fri" -"1996-04-19" "Fri" "Fri" "Fri_Sat" -"1996-04-20" "Sat" "Sat" "Sat_Sun" -"1996-04-21" "Sun" "Sun" "Sun_Mon" -"1996-04-22" "Mon" "Mon" "Mon_Tue" -"1996-04-23" "Tue" "Tue" "Tue_Wed" -"1996-04-24" "Wed" "Wed" "Wed_Thu" -"1996-04-25" "Thu" "Thu" "Thu_Fri" -"1996-04-26" "Fri" "Fri" "Fri_Sat" -"1996-04-27" "Sat" "Sat" "Sat_Sun" -"1996-04-28" "Sun" "Sun" "Sun_Mon" -"1996-04-29" "Mon" "Mon" "Mon_Tue" -"1996-04-30" "Tue" "Tue" "Tue_Hol" -"1996-05-01" "Wed" "Hol" 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"Sat" "Sat" "Sat_Sun" -"2003-09-07" "Sun" "Sun" "Sun_Mon" -"2003-09-08" "Mon" "Mon" "Mon_Tue" -"2003-09-09" "Tue" "Tue" "Tue_Wed" -"2003-09-10" "Wed" "Wed" "Wed_Thu" -"2003-09-11" "Thu" "Thu" "Thu_Fri" -"2003-09-12" "Fri" "Fri" "Fri_Sat" -"2003-09-13" "Sat" "Sat" "Sat_Sun" -"2003-09-14" "Sun" "Sun" "Sun_Mon" -"2003-09-15" "Mon" "Mon" "Mon_Tue" -"2003-09-16" "Tue" "Tue" "Tue_Wed" -"2003-09-17" "Wed" "Wed" "Wed_Thu" -"2003-09-18" "Thu" "Thu" "Thu_Fri" -"2003-09-19" "Fri" "Fri" "Fri_Sat" -"2003-09-20" "Sat" "Sat" "Sat_Sun" -"2003-09-21" "Sun" "Sun" "Sun_Mon" -"2003-09-22" "Mon" "Mon" "Mon_Tue" -"2003-09-23" "Tue" "Tue" "Tue_Wed" -"2003-09-24" "Wed" "Wed" "Wed_Thu" -"2003-09-25" "Thu" "Thu" "Thu_Fri" -"2003-09-26" "Fri" "Fri" "Fri_Sat" -"2003-09-27" "Sat" "Sat" "Sat_Sun" -"2003-09-28" "Sun" "Sun" "Sun_Mon" -"2003-09-29" "Mon" "Mon" "Mon_Tue" -"2003-09-30" "Tue" "Tue" "Tue_Wed" -"2003-10-01" "Wed" "Wed" "Wed_Thu" -"2003-10-02" "Thu" "Thu" "Thu_Fri" -"2003-10-03" "Fri" "Fri" "Fri_Sat" 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"Fri_Hol" -"2003-11-01" "Sat" "Hol" "Hol_Sun" -"2003-11-02" "Sun" "Sun" "Sun_Mon" -"2003-11-03" "Mon" "Mon" "Mon_Tue" -"2003-11-04" "Tue" "Tue" "Tue_Wed" -"2003-11-05" "Wed" "Wed" "Wed_Thu" -"2003-11-06" "Thu" "Thu" "Thu_Fri" -"2003-11-07" "Fri" "Fri" "Fri_Sat" -"2003-11-08" "Sat" "Sat" "Sat_Sun" -"2003-11-09" "Sun" "Sun" "Sun_Mon" -"2003-11-10" "Mon" "Mon" "Mon_Hol" -"2003-11-11" "Tue" "Hol" "Hol_Wed" -"2003-11-12" "Wed" "Wed" "Wed_Thu" -"2003-11-13" "Thu" "Thu" "Thu_Fri" -"2003-11-14" "Fri" "Fri" "Fri_Sat" -"2003-11-15" "Sat" "Sat" "Sat_Sun" -"2003-11-16" "Sun" "Sun" "Sun_Mon" -"2003-11-17" "Mon" "Mon" "Mon_Tue" -"2003-11-18" "Tue" "Tue" "Tue_Wed" -"2003-11-19" "Wed" "Wed" "Wed_Thu" -"2003-11-20" "Thu" "Thu" "Thu_Fri" -"2003-11-21" "Fri" "Fri" "Fri_Sat" -"2003-11-22" "Sat" "Sat" "Sat_Sun" -"2003-11-23" "Sun" "Sun" "Sun_Mon" -"2003-11-24" "Mon" "Mon" "Mon_Tue" -"2003-11-25" "Tue" "Tue" "Tue_Wed" -"2003-11-26" "Wed" "Wed" "Wed_Thu" -"2003-11-27" "Thu" "Thu" "Thu_Fri" -"2003-11-28" "Fri" "Fri" "Fri_Sat" -"2003-11-29" "Sat" "Sat" "Sat_Sun" -"2003-11-30" "Sun" "Sun" "Sun_Mon" -"2003-12-01" "Mon" "Mon" "Mon_Tue" -"2003-12-02" "Tue" "Tue" "Tue_Wed" -"2003-12-03" "Wed" "Wed" "Wed_Thu" -"2003-12-04" "Thu" "Thu" "Thu_Fri" -"2003-12-05" "Fri" "Fri" "Fri_Sat" -"2003-12-06" "Sat" "Sat" "Sat_Sun" -"2003-12-07" "Sun" "Sun" "Sun_Mon" -"2003-12-08" "Mon" "Mon" "Mon_Tue" -"2003-12-09" "Tue" "Tue" "Tue_Wed" -"2003-12-10" "Wed" "Wed" "Wed_Thu" -"2003-12-11" "Thu" "Thu" "Thu_Fri" -"2003-12-12" "Fri" "Fri" "Fri_Sat" -"2003-12-13" "Sat" "Sat" "Sat_Sun" -"2003-12-14" "Sun" "Sun" "Sun_Mon" -"2003-12-15" "Mon" "Mon" "Mon_Tue" -"2003-12-16" "Tue" "Tue" "Tue_Wed" -"2003-12-17" "Wed" "Wed" "Wed_Thu" -"2003-12-18" "Thu" "Thu" "Thu_Fri" -"2003-12-19" "Fri" "Fri" "Fri_Sat" -"2003-12-20" "Sat" "Sat" "Sat_Sun" -"2003-12-21" "Sun" "Sun" "Sun_Mon" -"2003-12-22" "Mon" "Mon" "Mon_Tue" -"2003-12-23" "Tue" "Tue" "Tue_Wed" -"2003-12-24" "Wed" "Wed" "Wed_Hol" -"2003-12-25" "Thu" "Hol" "Hol_Fri" 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"Thu_Fri" -"2004-01-23" "Fri" "Fri" "Fri_Sat" -"2004-01-24" "Sat" "Sat" "Sat_Sun" -"2004-01-25" "Sun" "Sun" "Sun_Mon" -"2004-01-26" "Mon" "Mon" "Mon_Tue" -"2004-01-27" "Tue" "Tue" "Tue_Wed" -"2004-01-28" "Wed" "Wed" "Wed_Thu" -"2004-01-29" "Thu" "Thu" "Thu_Fri" -"2004-01-30" "Fri" "Fri" "Fri_Sat" -"2004-01-31" "Sat" "Sat" "Sat_Sun" -"2004-02-01" "Sun" "Sun" "Sun_Mon" -"2004-02-02" "Mon" "Mon" "Mon_Tue" -"2004-02-03" "Tue" "Tue" "Tue_Wed" -"2004-02-04" "Wed" "Wed" "Wed_Thu" -"2004-02-05" "Thu" "Thu" "Thu_Fri" -"2004-02-06" "Fri" "Fri" "Fri_Sat" -"2004-02-07" "Sat" "Sat" "Sat_Sun" -"2004-02-08" "Sun" "Sun" "Sun_Mon" -"2004-02-09" "Mon" "Mon" "Mon_Tue" -"2004-02-10" "Tue" "Tue" "Tue_Wed" -"2004-02-11" "Wed" "Wed" "Wed_Thu" -"2004-02-12" "Thu" "Thu" "Thu_Fri" -"2004-02-13" "Fri" "Fri" "Fri_Sat" -"2004-02-14" "Sat" "Sat" "Sat_Sun" -"2004-02-15" "Sun" "Sun" "Sun_Mon" -"2004-02-16" "Mon" "Mon" "Mon_Tue" -"2004-02-17" "Tue" "Tue" "Tue_Wed" -"2004-02-18" "Wed" "Wed" "Wed_Thu" -"2004-02-19" "Thu" "Thu" "Thu_Fri" -"2004-02-20" "Fri" "Fri" "Fri_Sat" -"2004-02-21" "Sat" "Sat" "Sat_Sun" -"2004-02-22" "Sun" "Sun" "Sun_Mon" -"2004-02-23" "Mon" "Mon" "Mon_Tue" -"2004-02-24" "Tue" "Tue" "Tue_Wed" -"2004-02-25" "Wed" "Wed" "Wed_Thu" -"2004-02-26" "Thu" "Thu" "Thu_Fri" -"2004-02-27" "Fri" "Fri" "Fri_Sat" -"2004-02-28" "Sat" "Sat" "Sat_Sun" -"2004-02-29" "Sun" "Sun" "Sun_Mon" -"2004-03-01" "Mon" "Mon" "Mon_Tue" -"2004-03-02" "Tue" "Tue" "Tue_Wed" -"2004-03-03" "Wed" "Wed" "Wed_Thu" -"2004-03-04" "Thu" "Thu" "Thu_Fri" -"2004-03-05" "Fri" "Fri" "Fri_Sat" -"2004-03-06" "Sat" "Sat" "Sat_Sun" -"2004-03-07" "Sun" "Sun" "Sun_Mon" -"2004-03-08" "Mon" "Mon" "Mon_Tue" -"2004-03-09" "Tue" "Tue" "Tue_Wed" -"2004-03-10" "Wed" "Wed" "Wed_Thu" -"2004-03-11" "Thu" "Thu" "Thu_Fri" -"2004-03-12" "Fri" "Fri" "Fri_Sat" -"2004-03-13" "Sat" "Sat" "Sat_Sun" -"2004-03-14" "Sun" "Sun" "Sun_Mon" -"2004-03-15" "Mon" "Mon" "Mon_Tue" -"2004-03-16" "Tue" "Tue" "Tue_Wed" -"2004-03-17" "Wed" "Wed" "Wed_Thu" 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"Wed_Thu" -"2004-04-15" "Thu" "Thu" "Thu_Fri" -"2004-04-16" "Fri" "Fri" "Fri_Sat" -"2004-04-17" "Sat" "Sat" "Sat_Sun" -"2004-04-18" "Sun" "Sun" "Sun_Mon" -"2004-04-19" "Mon" "Mon" "Mon_Tue" -"2004-04-20" "Tue" "Tue" "Tue_Wed" -"2004-04-21" "Wed" "Wed" "Wed_Thu" -"2004-04-22" "Thu" "Thu" "Thu_Fri" -"2004-04-23" "Fri" "Fri" "Fri_Sat" -"2004-04-24" "Sat" "Sat" "Sat_Sun" -"2004-04-25" "Sun" "Sun" "Sun_Mon" -"2004-04-26" "Mon" "Mon" "Mon_Tue" -"2004-04-27" "Tue" "Tue" "Tue_Wed" -"2004-04-28" "Wed" "Wed" "Wed_Thu" -"2004-04-29" "Thu" "Thu" "Thu_Fri" -"2004-04-30" "Fri" "Fri" "Fri_Hol" -"2004-05-01" "Sat" "Hol" "Hol_Sun" -"2004-05-02" "Sun" "Sun" "Sun_Mon" -"2004-05-03" "Mon" "Mon" "Mon_Tue" -"2004-05-04" "Tue" "Tue" "Tue_Wed" -"2004-05-05" "Wed" "Wed" "Wed_Thu" -"2004-05-06" "Thu" "Thu" "Thu_Fri" -"2004-05-07" "Fri" "Fri" "Fri_Hol" -"2004-05-08" "Sat" "Hol" "Hol_Sun" -"2004-05-09" "Sun" "Sun" "Sun_Mon" -"2004-05-10" "Mon" "Mon" "Mon_Tue" -"2004-05-11" "Tue" "Tue" "Tue_Wed" -"2004-05-12" 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"Tue" "Tue" "Tue_Wed" -"2004-08-04" "Wed" "Wed" "Wed_Thu" -"2004-08-05" "Thu" "Thu" "Thu_Fri" -"2004-08-06" "Fri" "Fri" "Fri_Sat" -"2004-08-07" "Sat" "Sat" "Sat_Sun" -"2004-08-08" "Sun" "Sun" "Sun_Mon" -"2004-08-09" "Mon" "Mon" "Mon_Tue" -"2004-08-10" "Tue" "Tue" "Tue_Wed" -"2004-08-11" "Wed" "Wed" "Wed_Thu" -"2004-08-12" "Thu" "Thu" "Thu_Fri" -"2004-08-13" "Fri" "Fri" "Fri_Sat" -"2004-08-14" "Sat" "Sat" "Sat_Hol" -"2004-08-15" "Sun" "Hol" "Hol_Mon" -"2004-08-16" "Mon" "Mon" "Mon_Tue" -"2004-08-17" "Tue" "Tue" "Tue_Wed" -"2004-08-18" "Wed" "Wed" "Wed_Thu" -"2004-08-19" "Thu" "Thu" "Thu_Fri" -"2004-08-20" "Fri" "Fri" "Fri_Sat" -"2004-08-21" "Sat" "Sat" "Sat_Sun" -"2004-08-22" "Sun" "Sun" "Sun_Mon" -"2004-08-23" "Mon" "Mon" "Mon_Tue" -"2004-08-24" "Tue" "Tue" "Tue_Wed" -"2004-08-25" "Wed" "Wed" "Wed_Thu" -"2004-08-26" "Thu" "Thu" "Thu_Fri" -"2004-08-27" "Fri" "Fri" "Fri_Sat" -"2004-08-28" "Sat" "Sat" "Sat_Sun" -"2004-08-29" "Sun" "Sun" "Sun_Mon" -"2004-08-30" "Mon" "Mon" "Mon_Tue" 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"Hol_Mon" -"2011-05-02" "Mon" "Mon" "Mon_Tue" -"2011-05-03" "Tue" "Tue" "Tue_Wed" -"2011-05-04" "Wed" "Wed" "Wed_Thu" -"2011-05-05" "Thu" "Thu" "Thu_Fri" -"2011-05-06" "Fri" "Fri" "Fri_Sat" -"2011-05-07" "Sat" "Sat" "Sat_Hol" -"2011-05-08" "Sun" "Hol" "Hol_Mon" -"2011-05-09" "Mon" "Mon" "Mon_Tue" -"2011-05-10" "Tue" "Tue" "Tue_Wed" -"2011-05-11" "Wed" "Wed" "Wed_Thu" -"2011-05-12" "Thu" "Thu" "Thu_Fri" -"2011-05-13" "Fri" "Fri" "Fri_Sat" -"2011-05-14" "Sat" "Sat" "Sat_Sun" -"2011-05-15" "Sun" "Sun" "Sun_Mon" -"2011-05-16" "Mon" "Mon" "Mon_Tue" -"2011-05-17" "Tue" "Tue" "Tue_Wed" -"2011-05-18" "Wed" "Wed" "Wed_Thu" -"2011-05-19" "Thu" "Thu" "Thu_Fri" -"2011-05-20" "Fri" "Fri" "Fri_Sat" -"2011-05-21" "Sat" "Sat" "Sat_Sun" -"2011-05-22" "Sun" "Sun" "Sun_Mon" -"2011-05-23" "Mon" "Mon" "Mon_Tue" -"2011-05-24" "Tue" "Tue" "Tue_Wed" -"2011-05-25" "Wed" "Wed" "Wed_Thu" -"2011-05-26" "Thu" "Thu" "Thu_Fri" -"2011-05-27" "Fri" "Fri" "Fri_Sat" -"2011-05-28" "Sat" "Sat" "Sat_Sun" -"2011-05-29" "Sun" "Sun" "Sun_Mon" -"2011-05-30" "Mon" "Mon" "Mon_Tue" -"2011-05-31" "Tue" "Tue" "Tue_Wed" -"2011-06-01" "Wed" "Wed" "Wed_Hol" -"2011-06-02" "Thu" "Hol" "Hol_Fri" -"2011-06-03" "Fri" "Fri" "Fri_Sat" -"2011-06-04" "Sat" "Sat" "Sat_Sun" -"2011-06-05" "Sun" "Sun" "Sun_Mon" -"2011-06-06" "Mon" "Mon" "Mon_Tue" -"2011-06-07" "Tue" "Tue" "Tue_Wed" -"2011-06-08" "Wed" "Wed" "Wed_Thu" -"2011-06-09" "Thu" "Thu" "Thu_Fri" -"2011-06-10" "Fri" "Fri" "Fri_Sat" -"2011-06-11" "Sat" "Sat" "Sat_Sun" -"2011-06-12" "Sun" "Sun" "Sun_Hol" -"2011-06-13" "Mon" "Hol" "Hol_Tue" -"2011-06-14" "Tue" "Tue" "Tue_Wed" -"2011-06-15" "Wed" "Wed" "Wed_Thu" -"2011-06-16" "Thu" "Thu" "Thu_Fri" -"2011-06-17" "Fri" "Fri" "Fri_Sat" -"2011-06-18" "Sat" "Sat" "Sat_Sun" -"2011-06-19" "Sun" "Sun" "Sun_Mon" -"2011-06-20" "Mon" "Mon" "Mon_Tue" -"2011-06-21" "Tue" "Tue" "Tue_Wed" -"2011-06-22" "Wed" "Wed" "Wed_Thu" -"2011-06-23" "Thu" "Thu" "Thu_Fri" -"2011-06-24" "Fri" "Fri" "Fri_Sat" -"2011-06-25" "Sat" "Sat" "Sat_Sun" 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"Sat_Sun" -"2011-07-24" "Sun" "Sun" "Sun_Mon" -"2011-07-25" "Mon" "Mon" "Mon_Tue" -"2011-07-26" "Tue" "Tue" "Tue_Wed" -"2011-07-27" "Wed" "Wed" "Wed_Thu" -"2011-07-28" "Thu" "Thu" "Thu_Fri" -"2011-07-29" "Fri" "Fri" "Fri_Sat" -"2011-07-30" "Sat" "Sat" "Sat_Sun" -"2011-07-31" "Sun" "Sun" "Sun_Mon" -"2011-08-01" "Mon" "Mon" "Mon_Tue" -"2011-08-02" "Tue" "Tue" "Tue_Wed" -"2011-08-03" "Wed" "Wed" "Wed_Thu" -"2011-08-04" "Thu" "Thu" "Thu_Fri" -"2011-08-05" "Fri" "Fri" "Fri_Sat" -"2011-08-06" "Sat" "Sat" "Sat_Sun" -"2011-08-07" "Sun" "Sun" "Sun_Mon" -"2011-08-08" "Mon" "Mon" "Mon_Tue" -"2011-08-09" "Tue" "Tue" "Tue_Wed" -"2011-08-10" "Wed" "Wed" "Wed_Thu" -"2011-08-11" "Thu" "Thu" "Thu_Fri" -"2011-08-12" "Fri" "Fri" "Fri_Sat" -"2011-08-13" "Sat" "Sat" "Sat_Sun" -"2011-08-14" "Sun" "Sun" "Sun_Hol" -"2011-08-15" "Mon" "Hol" "Hol_Tue" -"2011-08-16" "Tue" "Tue" "Tue_Wed" -"2011-08-17" "Wed" "Wed" "Wed_Thu" -"2011-08-18" "Thu" "Thu" "Thu_Fri" -"2011-08-19" "Fri" "Fri" "Fri_Sat" -"2011-08-20" "Sat" "Sat" "Sat_Sun" -"2011-08-21" "Sun" "Sun" "Sun_Mon" -"2011-08-22" "Mon" "Mon" "Mon_Tue" -"2011-08-23" "Tue" "Tue" "Tue_Wed" -"2011-08-24" "Wed" "Wed" "Wed_Thu" -"2011-08-25" "Thu" "Thu" "Thu_Fri" -"2011-08-26" "Fri" "Fri" "Fri_Sat" -"2011-08-27" "Sat" "Sat" "Sat_Sun" -"2011-08-28" "Sun" "Sun" "Sun_Mon" -"2011-08-29" "Mon" "Mon" "Mon_Tue" -"2011-08-30" "Tue" "Tue" "Tue_Wed" -"2011-08-31" "Wed" "Wed" "Wed_Thu" -"2011-09-01" "Thu" "Thu" "Thu_Fri" -"2011-09-02" "Fri" "Fri" "Fri_Sat" -"2011-09-03" "Sat" "Sat" "Sat_Sun" -"2011-09-04" "Sun" "Sun" "Sun_Mon" -"2011-09-05" "Mon" "Mon" "Mon_Tue" -"2011-09-06" "Tue" "Tue" "Tue_Wed" -"2011-09-07" "Wed" "Wed" "Wed_Thu" -"2011-09-08" "Thu" "Thu" "Thu_Fri" -"2011-09-09" "Fri" "Fri" "Fri_Sat" -"2011-09-10" "Sat" "Sat" "Sat_Sun" -"2011-09-11" "Sun" "Sun" "Sun_Mon" -"2011-09-12" "Mon" "Mon" "Mon_Tue" -"2011-09-13" "Tue" "Tue" "Tue_Wed" -"2011-09-14" "Wed" "Wed" "Wed_Thu" -"2011-09-15" "Thu" "Thu" "Thu_Fri" -"2011-09-16" "Fri" "Fri" "Fri_Sat" 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"Fri_Sat" -"2011-10-15" "Sat" "Sat" "Sat_Sun" -"2011-10-16" "Sun" "Sun" "Sun_Mon" -"2011-10-17" "Mon" "Mon" "Mon_Tue" -"2011-10-18" "Tue" "Tue" "Tue_Wed" -"2011-10-19" "Wed" "Wed" "Wed_Thu" -"2011-10-20" "Thu" "Thu" "Thu_Fri" -"2011-10-21" "Fri" "Fri" "Fri_Sat" -"2011-10-22" "Sat" "Sat" "Sat_Sun" -"2011-10-23" "Sun" "Sun" "Sun_Mon" -"2011-10-24" "Mon" "Mon" "Mon_Tue" -"2011-10-25" "Tue" "Tue" "Tue_Wed" -"2011-10-26" "Wed" "Wed" "Wed_Thu" -"2011-10-27" "Thu" "Thu" "Thu_Fri" -"2011-10-28" "Fri" "Fri" "Fri_Sat" -"2011-10-29" "Sat" "Sat" "Sat_Sun" -"2011-10-30" "Sun" "Sun" "Sun_Mon" -"2011-10-31" "Mon" "Mon" "Mon_Hol" -"2011-11-01" "Tue" "Hol" "Hol_Wed" -"2011-11-02" "Wed" "Wed" "Wed_Thu" -"2011-11-03" "Thu" "Thu" "Thu_Fri" -"2011-11-04" "Fri" "Fri" "Fri_Sat" -"2011-11-05" "Sat" "Sat" "Sat_Sun" -"2011-11-06" "Sun" "Sun" "Sun_Mon" -"2011-11-07" "Mon" "Mon" "Mon_Tue" -"2011-11-08" "Tue" "Tue" "Tue_Wed" -"2011-11-09" "Wed" "Wed" "Wed_Thu" -"2011-11-10" "Thu" "Thu" "Thu_Hol" -"2011-11-11" "Fri" "Hol" "Hol_Sat" -"2011-11-12" "Sat" "Sat" "Sat_Sun" -"2011-11-13" "Sun" "Sun" "Sun_Mon" -"2011-11-14" "Mon" "Mon" "Mon_Tue" -"2011-11-15" "Tue" "Tue" "Tue_Wed" -"2011-11-16" "Wed" "Wed" "Wed_Thu" -"2011-11-17" "Thu" "Thu" "Thu_Fri" -"2011-11-18" "Fri" "Fri" "Fri_Sat" -"2011-11-19" "Sat" "Sat" "Sat_Sun" -"2011-11-20" "Sun" "Sun" "Sun_Mon" -"2011-11-21" "Mon" "Mon" "Mon_Tue" -"2011-11-22" "Tue" "Tue" "Tue_Wed" -"2011-11-23" "Wed" "Wed" "Wed_Thu" -"2011-11-24" "Thu" "Thu" "Thu_Fri" -"2011-11-25" "Fri" "Fri" "Fri_Sat" -"2011-11-26" "Sat" "Sat" "Sat_Sun" -"2011-11-27" "Sun" "Sun" "Sun_Mon" -"2011-11-28" "Mon" "Mon" "Mon_Tue" -"2011-11-29" "Tue" "Tue" "Tue_Wed" -"2011-11-30" "Wed" "Wed" "Wed_Thu" -"2011-12-01" "Thu" "Thu" "Thu_Fri" -"2011-12-02" "Fri" "Fri" "Fri_Sat" -"2011-12-03" "Sat" "Sat" "Sat_Sun" -"2011-12-04" "Sun" "Sun" "Sun_Mon" -"2011-12-05" "Mon" "Mon" "Mon_Tue" -"2011-12-06" "Tue" "Tue" "Tue_Wed" -"2011-12-07" "Wed" "Wed" "Wed_Thu" -"2011-12-08" "Thu" "Thu" "Thu_Fri" -"2011-12-09" "Fri" "Fri" "Fri_Sat" -"2011-12-10" "Sat" "Sat" "Sat_Sun" -"2011-12-11" "Sun" "Sun" "Sun_Mon" -"2011-12-12" "Mon" "Mon" "Mon_Tue" -"2011-12-13" "Tue" "Tue" "Tue_Wed" -"2011-12-14" "Wed" "Wed" "Wed_Thu" -"2011-12-15" "Thu" "Thu" "Thu_Fri" -"2011-12-16" "Fri" "Fri" "Fri_Sat" -"2011-12-17" "Sat" "Sat" "Sat_Sun" -"2011-12-18" "Sun" "Sun" "Sun_Mon" -"2011-12-19" "Mon" "Mon" "Mon_Tue" -"2011-12-20" "Tue" "Tue" "Tue_Wed" -"2011-12-21" "Wed" "Wed" "Wed_Thu" -"2011-12-22" "Thu" "Thu" "Thu_Fri" -"2011-12-23" "Fri" "Fri" "Fri_Sat" -"2011-12-24" "Sat" "Sat" "Sat_Hol" -"2011-12-25" "Sun" "Hol" "Hol_Mon" -"2011-12-26" "Mon" "Mon" "Mon_Tue" -"2011-12-27" "Tue" "Tue" "Tue_Wed" -"2011-12-28" "Wed" "Wed" "Wed_Thu" -"2011-12-29" "Thu" "Thu" "Thu_Fri" -"2011-12-30" "Fri" "Fri" "Fri_Sat" -"2011-12-31" "Sat" "Sat" "Sat_Hol" diff --git a/old_C_code/stage2_UNFINISHED/src/unused/datesall.txt b/old_C_code/stage2_UNFINISHED/src/unused/datesall.txt deleted file mode 100644 index e4622a9..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/datesall.txt +++ /dev/null @@ -1,43721 +0,0 @@ -01JAN2009:00:00:00 -01JAN2009:00:30:00 -01JAN2009:01:00:00 -01JAN2009:01:30:00 -01JAN2009:02:00:00 -01JAN2009:02:30:00 -01JAN2009:03:00:00 -01JAN2009:03:30:00 -01JAN2009:04:00:00 -01JAN2009:04:30:00 -01JAN2009:05:00:00 -01JAN2009:05:30:00 -01JAN2009:06:00:00 -01JAN2009:06:30:00 -01JAN2009:07:00:00 -01JAN2009:07:30:00 -01JAN2009:08:00:00 -01JAN2009:08:30:00 -01JAN2009:09:00:00 -01JAN2009:09:30:00 -01JAN2009:10:00:00 -01JAN2009:10:30:00 -01JAN2009:11:00:00 -01JAN2009:11:30:00 -01JAN2009:12:00:00 -01JAN2009:12:30:00 -01JAN2009:13:00:00 -01JAN2009:13:30:00 -01JAN2009:14:00:00 -01JAN2009:14:30:00 -01JAN2009:15:00:00 -01JAN2009:15:30:00 -01JAN2009:16:00:00 -01JAN2009:16:30:00 -01JAN2009:17:00:00 -01JAN2009:17:30:00 -01JAN2009:18:00:00 -01JAN2009:18:30:00 -01JAN2009:19:00:00 -01JAN2009:19:30:00 -01JAN2009:20:00:00 -01JAN2009:20:30:00 -01JAN2009:21:00:00 -01JAN2009:21:30:00 -01JAN2009:22:00:00 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-#library(data.table) -#d10 <- fread("~/tmp/data/2010.csv") -#tot <- d10[, list(tot= sum(CPP_PUISSANCE_BRUTE), -# min= min(CPP_PUISSANCE_BRUTE), -# max= max(CPP_PUISSANCE_BRUTE), -# sd = sd( CPP_PUISSANCE_BRUTE)), -# by = FK_CCU_ID] -# write.table(file = "~/tmp/data/totconso-par-indiv.txt", tot) - -tot <- read.table("~/tmp/data/totconso-par-indiv.txt") -synchro <- read.table("~/tmp/data/syncrone2010.txt") -mat_synchro <- matrix(c(synchro$V1[1:2], synchro$V1) / 1e6, - ncol = 48, - byrow = TRUE) - -xHour <- seq(1/2, 24, length.out = 48) -xDate <- seq.Date(from = as.Date("2010/1/1"), - to = as.Date("2011/1/1"), - length.out = 365 * 48 - 2) - -# Filter infra daily -plot(xDate, filter(synchro$V1 / 1e6, rep(1 / 48, 48)), - type = 'l', - main = 'Synchrone 2010', - ylab = 'Charge (en Mwh)', - xlab = 'Time') - -# Daily -matplot(xHour, scale(t(mat_synchro)), type = 'l', lty = 1, - col = rgb(0, 0, 0, .25), - main = 'Synchrone jounalière (norm.)', - xlab = 'Heures', ylab = 'Charge (en Mwh)') - - -tot_order <- tot[order(tot$tot), ] -max_order <- tot[order(tot$max), ] - -## Mesures aberrantes ? -tail(cbind(max_order[,1], max_order[,-1] / 1e3)) - - -## I want to recuperate ids: 186733, 191819, 193174 -## which corresponds to places 1, 2481, 3478 -# identiants <- read.table('~/tmp/data/identifs.txt') -# match(c(186733, 191819, 193174), identiants$V1) - -#c186733 <- d10[, FK_CCU_ID == 186733] -#c191819 <- d10[, FK_CCU_ID == 191819] -#c193174 <- d10[, FK_CCU_ID == 193174] - -c186733 <- read.csv("~/tmp/data/c186733.csv")[, 4] / 1e3 - -mat_186733 <- matrix(c(c186733[1:2], c186733), - ncol = 48, - byrow = TRUE) - -# Filter infra daily -plot(xDate, filter(c186733, rep(1 / 48, 48)), - type = 'l', - main = "Consommation individuelle (2010)", - ylab = 'Charge (en Mwh)', - xlab = 'Temps') - -# Daily (raw) -matplot(xHour, t(mat_186733), type = 'l', lty = 1, - col = rgb(0, 0, 0, .25), - #main = "Consommation individuelle journalière " - xlab = 'Heures', ylab = 'Charge (en Mwh)') - -# Daily (standarized) -matplot(xHour, scale(t(mat_186733)), type = 'l', lty = 1, - col = rgb(0, 0, 0, .25), - xlab = 'Heures', ylab = 'Charge (en Mwh)') diff --git a/old_C_code/stage2_UNFINISHED/src/unused/effectives-per-classe.r b/old_C_code/stage2_UNFINISHED/src/unused/effectives-per-classe.r deleted file mode 100644 index a63a8fb..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/effectives-per-classe.r +++ /dev/null @@ -1,6 +0,0 @@ -# compute the effectives per class - -load("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/res/clfit200RC.Rdata") -clustfactor <- read.table("~/tmp/clustfactorWER15.txt") - -aggregate(c(table(res[,1])), by = list(clustfactor$x), FUN = sum) diff --git a/old_C_code/stage2_UNFINISHED/src/unused/identifs.txt b/old_C_code/stage2_UNFINISHED/src/unused/identifs.txt deleted file mode 100644 index 6526f54..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/identifs.txt +++ /dev/null @@ -1,25011 +0,0 @@ -186733 -186734 -186735 -186737 -186738 -186740 -186741 -186742 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-223280 -223282 -223283 diff --git a/old_C_code/stage2_UNFINISHED/src/unused/sowas-superseded.r b/old_C_code/stage2_UNFINISHED/src/unused/sowas-superseded.r deleted file mode 100644 index dca3353..0000000 --- a/old_C_code/stage2_UNFINISHED/src/unused/sowas-superseded.r +++ /dev/null @@ -1,1563 +0,0 @@ -########################################################### -# CONTINUOUS WAVELET TRANSFORMATION OF A TIME SERIES OBJECT -########################################################### - -cwt.ts <- function(ts,s0,noctave=5,nvoice=10,w0=2*pi){ - - if (class(ts)!="ts"){ - - cat("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n") - - } - else{ - - t=time(ts) - dt=t[2]-t[1] - - s0unit=s0/dt*w0/(2*pi) - s0log=as.integer((log2(s0unit)-1)*nvoice+1.5) - - if (s0log<1){ - cat(paste("# s0unit = ",s0unit,"\n",sep="")) - cat(paste("# s0log = ",s0log,"\n",sep="")) - cat("# s0 too small for w0! \n") - } - totnoct=noctave+as.integer(s0log/nvoice)+1 - - totts.cwt=cwt(ts,totnoct,nvoice,w0,plot=0) - - ts.cwt=totts.cwt[,s0log:(s0log+noctave*nvoice)] - - #Normalization - sqs <- sqrt(2^(0:(noctave*nvoice)/nvoice)*s0) - smat <- matrix(rep(sqs,length(t)),nrow=length(t),byrow=TRUE) - - ts.cwt*smat - - } - -} - -##################################### -# WSP -##################################### - -wsp <- function(ts,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,siglevel=0.95,critval=NULL,nreal=1000,arealsiglevel=0.9,kernel=0,markt=-999,marks=-999,logscale=FALSE,plot=TRUE,units="",device="screen",file="wsp",color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=3){ - - if (class(ts)!="ts"){ - - cat("# This function needs a time series object as input. You may construct this by using the function ts(data,start,deltat). Try '?ts' for help.\n") - - } - else{ - - if (sw!=0 & swabs==0) - swabs <- as.integer(sw*nvoice) - if (swabs!=0 & sw==0) - sw <- swabs/nvoice - - sllist <- checkarealsiglevel(sw,tw,w0,arealsiglevel,siglevel,0) - arealsiglevel <- sllist$arealsiglevel - siglevel <- sllist$siglevel - - at <- NULL - - t <- time(ts) - dt <- deltat(ts) - s0rem <- s0 - s0 <- adjust.s0(s0,dt) - dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1 - - noctave <- adjust.noctave(length(ts),dt,s0,tw,noctave) - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - tsanom <- ts-mean(ts) - - #WAVELET TRANSFORMATION - ts.cwt <- cwt.ts(tsanom,s0,noctave,nvoice,w0) - - #SMOOTHING - wsp <- smooth.matrix(Mod(ts.cwt)^2,swabs) - smwsp <- smooth.time(wsp,tw,dt,scalevector) - - #POINTWISE SIGNIFICANCE TEST - if (is.null(critval)==FALSE){ # is critval empty? - if (dim(critval)[2]!=dim(smwsp)[2]){ # is critval of the wrong dimension? - if (siglevel[1]!=0 & nreal!=0) critval <- - criticalvaluesWSP(tsanom,s0,noctave,nvoice,w0,swabs,tw,siglevel,nreal) - #critval is of wrong dimension and siglevel and nreal are given - else { - critval <- NULL # no test possible - arealsiglevel <- 0 - cat("# dimension of critval is wrong \n") - cat("# areawise test only possible with pointwise test \n") - } - } - } - else{ # critval is empty, but nreal or siglevel is given - if (siglevel[1]!=0 & nreal!=0) critval <- - criticalvaluesWSP(tsanom,s0,noctave,nvoice,w0,swabs,tw,siglevel,nreal) - else { - critval <- NULL - arealsiglevel <- 0 - cat("# areawise test only possible with pointwise test \n") - } - } - - #AREAL SIGNIFICANCE TEST - if (arealsiglevel!=0){ - v <- critval[1,] - v[v==0] <- 9999 - cvmat <- matrix(rep(v,length(t)),nrow=length(t),byrow=TRUE) - atest <- arealtest(smwsp/cvmat,dt,s0,noctave,nvoice,w0,swabs,tw,siglevel,arealsiglevel,kernel,0) - at <- atest$at - kernel <- atest$kernel - } - - if (s0rem<s0){ - smwsp <- addvalues(nvoice,dnoctave,smwsp,NA) - critval <- addvalues(nvoice,dnoctave,critval,1) - #at <- addvalues(nvoice,dnoctave,at,NA) - noctave <- noctave+dnoctave - s0 <- s0/2^dnoctave - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - } - - #PARAMETERS - wclist <- - list(modulus=smwsp,phase=NULL,time=t,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,scales=scalevector,critval=critval,at=at,kernel=kernel) - - class(wclist) <- "wt" - - #PLOTTING - if (plot) - plot(wclist,markt,marks,NULL,NULL,logscale,FALSE,units,"Wavelet Power Spectrum",device,file,FALSE,color,pwidth,pheight,labsc,labtext,sigplot) - - wclist - - } - -} - -##################################### -# WCO -##################################### - -wco <- - function(ts1,ts2,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,siglevel=0.95,arealsiglevel=0.9,kernel=0,markt=-999,marks=-999,sqr=FALSE,phase=TRUE,plot=TRUE,units="",device="screen",file="wcoh",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=3){ - - if (class(ts1)!="ts"){ - - cat("# This function needs two time series objects as input. You may construct them by using the function ts(data,start,deltat). Try '?ts' for help.\n") - - } - else{ - - if (sw!=0 & swabs==0) - swabs <- as.integer(sw*nvoice) - if (swabs!=0 & sw==0) - sw <- swabs/nvoice - - sllist <- checkarealsiglevel(sw,tw,w0,arealsiglevel,siglevel,1) - arealsiglevel <- sllist$arealsiglevel - siglevel <- sllist$siglevel - - if (sw==0 & tw==0 & swabs==0) { - cat("# coherence without averaging makes no sense! \n") - siglevel <- 0 - arealsiglevel <- 0 - } - - if (phase==FALSE) phs <- NULL - - at <- NULL - - tsadjust <- adjust.timeseries(ts1,ts2) - ts1 <- tsadjust$ts1 - ts2 <- tsadjust$ts2 - - t <- time(ts1) - dt <- deltat(ts1) - - s0rem <- s0 - s0 <- adjust.s0(s0,dt) - dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1 - - noctave <- adjust.noctave(length(ts1),dt,s0,tw,noctave) - - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - - ts1anom <- ts1-mean(ts1) - ts2anom <- ts2-mean(ts2) - - ts1.cwt <- cwt.ts(ts1anom,s0,noctave,nvoice,w0) - ts2.cwt <- cwt.ts(ts2anom,s0,noctave,nvoice,w0) - - cosp <- Re(ts1.cwt)*Re(ts2.cwt) + Im(ts1.cwt)*Im(ts2.cwt) - quad <- Im(ts1.cwt)*Re(ts2.cwt) - Re(ts1.cwt)*Im(ts2.cwt) - wsp1 <- Mod(ts1.cwt)^2 - wsp2 <- Mod(ts2.cwt)^2 - - smcosp <- smooth.matrix(cosp,swabs) - smquad <- smooth.matrix(quad,swabs) - smwsp1 <- smooth.matrix(wsp1,swabs) - smwsp2 <- smooth.matrix(wsp2,swabs) - - smsmcosp <- smooth.time(smcosp,tw,dt,scalevector) - smsmquad <- smooth.time(smquad,tw,dt,scalevector) - smsmwsp1 <- smooth.time(smwsp1,tw,dt,scalevector) - smsmwsp2 <- smooth.time(smwsp2,tw,dt,scalevector) - - if (sqr==FALSE) - wcoh <- sqrt((smsmcosp^2+smsmquad^2)/(smsmwsp1*smsmwsp2)) - else - wcoh <- (smsmcosp^2+smsmquad^2)/(smsmwsp1*smsmwsp2) - - if (phase) - phs <- atan2(smsmquad,smsmcosp) - else phs <- NULL - - #POINTWISE SIGNIFICANCE TEST - if (siglevel[1]!=0) critval <- criticalvaluesWCO(s0,noctave,nvoice,w0,swabs,tw,siglevel) - else critval <- NULL - if (sqr==TRUE & is.null(critval)==FALSE) - critval <- critval^2 - - #AREAWISE SIGNIFICANCE TEST - if (arealsiglevel!=0){ - atest <- arealtest(wcoh/critval,dt,s0,noctave,nvoice,w0,swabs,tw,siglevel,arealsiglevel,kernel,1) - at <- atest$at - kernel <- atest$kernel - } - - wcoh[1,1] <- 0 - wcoh[1,2] <- 1 - - if (phase){ - phs[1,1] <- -pi - phs[1,2] <- pi - } - - if (s0rem<s0){ - wcoh <- addvalues(nvoice,dnoctave,wcoh,NA) - phs <- addvalues(nvoice,dnoctave,phs,NA) - noctave <- noctave+dnoctave - s0 <- s0/2^dnoctave - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - } - - wclist <- list(modulus=wcoh,phase=phs,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,time=t,scales=scalevector,critval=critval,at=at,kernel=kernel) - - class(wclist) <- "wt" - - if (plot) plot(wclist,markt,marks,NULL,NULL,FALSE,phase,units,"Wavelet Coherence",device,file,split,color,pwidth,pheight,labsc,labtext,sigplot) - - wclist - - } - - } - -##################################### -# WCS -##################################### - -wcs <- function(ts1,ts2,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,markt=-999,marks=-999,logscale=FALSE,phase=TRUE,plot=TRUE,units="",device="screen",file="wcsp",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext=""){ - - if (class(ts1)!="ts"){ - - cat("# This function needs two time series objects as input. You may construct them by using the function ts(data,start,deltat). Try '?ts' for help. \n") - - } - else{ - - if (sw!=0 & swabs==0) - swabs <- as.integer(sw*nvoice) - if (swabs!=0 & sw==0) - sw <- swabs/nvoice - - tsadjust <- adjust.timeseries(ts1,ts2) - ts1 <- tsadjust$ts1 - ts2 <- tsadjust$ts2 - - t <- time(ts1) - dt <- deltat(ts1) - - s0rem <- s0 - s0 <- adjust.s0(s0,dt) - dnoctave <- as.integer(log(s0/s0rem-0.000000000001)/log(2))+1 - - noctave <- adjust.noctave(length(ts1),dt,s0,tw,noctave) - - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - - ts1anom <- ts1-mean(ts1) - ts2anom <- ts2-mean(ts2) - - ts1.cwt <- cwt.ts(ts1anom,s0,noctave,nvoice,w0) - ts2.cwt <- cwt.ts(ts2anom,s0,noctave,nvoice,w0) - - cosp <- Re(ts1.cwt)*Re(ts2.cwt) + Im(ts1.cwt)*Im(ts2.cwt) - quad <- Im(ts1.cwt)*Re(ts2.cwt) - Re(ts1.cwt)*Im(ts2.cwt) - - smcosp <- smooth.matrix(cosp,swabs) - smquad <- smooth.matrix(quad,swabs) - - smsmcosp <- smooth.time(smcosp,tw,dt,scalevector) - smsmquad <- smooth.time(smquad,tw,dt,scalevector) - - wcsp <- smsmcosp^2+smsmquad^2 - - if (phase) - phs <- atan2(smsmquad,smsmcosp) - else phs <- NULL - - if (phase){ - phs[1,1] <- -pi - phs[1,2] <- pi - } - - if (s0rem<s0){ - wcsp <- addvalues(nvoice,dnoctave,wcoh,NA) - phs <- addvalues(nvoice,dnoctave,phs,NA) - noctave <- noctave+dnoctave - s0 <- s0/2^dnoctave - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - } - - wclist <- list(modulus=wcsp,phase=phs,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,time=t,scales=scalevector,critval=NULL,at=NULL,kernel=NULL) - - class(wclist) <- "wt" - - if (plot) plot(wclist,markt,marks,NULL,NULL,logscale,phase,units,"Wavelet Cross Spectrum",device,file,split,color,pwidth,pheight,labsc,labtext) - - wclist - - } - -} - -########################################## -# POINTWISE SIGNIFICANCE TEST -########################################## - -rawWSP <- function(ts,s0=1,noctave=5,nvoice=20,w0=2*pi,swabs=0,tw=0,scalevector){ - - tsanom <- ts-mean(ts) - - ts.cwt <- cwt.ts(tsanom,s0,noctave,nvoice,w0) - - wsp <- Mod(ts.cwt)^2 - - smwsp <- smooth.matrix(wsp,swabs) - smsmwsp <- smooth.time(smwsp,tw,deltat(ts),scalevector) - - smsmwsp - -} - -criticalvaluesWSP <- function(ts,s0=1,noctave=5,nvoice=10,w0=2*pi,swabs=0,tw=0,siglevel=0.95,nreal=1000){ - - t=time(ts) - dt=deltat(ts) - - s0 <- adjust.s0(s0,dt) - noctave <- adjust.noctave(length(ts),dt,s0,tw,noctave) - - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - - cat("# calculating critical values by means of MC-simulations \n") - - s0unit=s0/dt*w0/(2*pi) - s0log=as.integer((log2(s0unit)-1)*nvoice+1.5) - - wsp0 <- rawWSP(ts,s0,noctave,nvoice,w0,swabs,tw,scalevector) - - S <- dim(wsp0)[2] - - n1 <- 1 + 2*as.integer( sqrt(2) * 2^((S-swabs+s0log-1)/nvoice+1) ) - n2 <- max(scalevector)*tw*2/dt*1.1 - n3 <- 2*tw*s0*2^noctave/dt+100 - n <- max(n1,n2,n3) - - center <- (n-1)/2+1 - cv <- matrix(rep(0,S*length(siglevel)),ncol=S) - rmatrix <- matrix(0,nrow=nreal,ncol=S) - - # Fitting AR1-process (red noise) to data - arts0 <- ar(ts,order.max=1) - sdts0 <- sd(ts[1:length(ts)]) - - if (arts0$order==0){ - se <- sqrt(arts0$var) - arts0$ar <- 0.000001 - } - else - se <- sqrt(sdts0*sdts0*(1-arts0$ar*arts0$ar)) - - tsMC <- ts(data=rep(0,n),start=t[1],frequency=1/dt) - - # MC Simulations - for (i in 1:nreal){ - - tsMC[1:n] <- arima.sim(list(ar = arts0$ar), n+1, sd = se)[2:(n+1)] - - rmatrix[i,] <- rawWSP(tsMC,s0,noctave,nvoice,w0,swabs,tw,scalevector)[center,] - - } - - for (s in (1+swabs):(S-swabs)) rmatrix[,s] <- sort(rmatrix[,s]) - - for (i in 1:length(siglevel)){ - sigindex <- as.integer(nreal*siglevel[i]) - cvv <- rmatrix[sigindex,] - cvv[is.na(cvv)] <- 0 - cv[i,] <- cvv - } - - cv - -} - -########### - -criticalvaluesWCO <- function(s0,noctave,nvoice,w0,swabs,tw,siglevel=0.95){ - - cv=rep(0,length(siglevel)) - - for (i in 1:length(siglevel)){ - - if (siglevel[i]!=0.9 && siglevel[i]!=0.95 && siglevel[i]!=0.99) siglevel[i] <- 0.95 - - if (siglevel[i]==0.9){ - cat("# significance level set to 0.90 \n") - sw <- 1.0*swabs/nvoice - cv[i] <- 0.00246872*w0^2*sw + 0.0302419*w0*sw^2 + 0.342056*sw^3 - - 0.000425853*w0^2 - 0.101749*w0*sw - 0.703537*sw^2 + - 0.00816219*w0 + 0.442342*sw + 0.970315 - } - - if (siglevel[i]==0.95){ - cat("# significance level set to 0.95 \n") - sw <- swabs*100.0/3.0/nvoice - cv[i] <- 0.0000823*w0^3 + 0.0000424*w0^2*sw + 0.0000113*w0*sw^2 + - 0.0000154*sw^3 - 0.0023*w0^2 - 0.00219*w0*sw - 0.000751*sw^2 + - 0.0205*w0 + 0.0127*sw + 0.95 - } - - if (siglevel[i]==0.99){ - cat("# significance level set to 0.99 \n") - sw <- 1.0*swabs/nvoice - cv[i] <- 0.00102304*w0^2*sw + 0.00745772*w0*sw^2 + 0.230035*sw^3 - - 0.000361565*w0^2 - 0.0502861*w0*sw - 0.440777*sw^2 + - 0.00694998*w0 + 0.29643*sw + 0.972964 - } - - if (cv[i]>1) cv[i] <- 1 - - cat(paste("# significance testing, cv=",cv[i]," \n",sep="")) - - } - - cv - -} - -############################# -# AREAWISE SIGNIFICANCE TEST -############################# - -slide <- function(data,kernellist,s0,noctave,nvoice,cv){ - - # slides kernel over data set - #---------------------------- - # data: matrix containing data - # kernellist: matrix containing kernel - # s0: lowest scale - # noctave: number of octaves - # nvoice: number of voices per octave - # cv: critical value, all values higher are set to one - - #Time: (rows) n,i the kernel is to be scaled in this direction - #Scale: (cols) m,j - - data[data<cv] <- 0 - - kernel <- kernellist$bitmap - - js <- kernellist$is - - sm <- s0*2^noctave - - dbin <- tobin(data) - kbin <- tobin(kernel) - - dn <- nrow(dbin) - dm <- ncol(dbin) - - kn <- nrow(kbin) - km <- ncol(kbin) - - mark <- matrix(rep(0,dn*dm),nrow=dn) - - for (j in 1:(dm-km+1)){ - - s <- s0*2^((j+js-1)/nvoice) - kscn <- as.integer(kn*s/sm); - if (kscn==0) kscn <- 1 - - ksc <- scaleKernel(kbin,kscn) - kscm <- km - - for (i in 1:(dn-kscn+1)){ - - subbin <- dbin[i:(i+kscn-1),j:(j+kscm-1)] - - if (sum(ksc*subbin)==sum(ksc)) - mark[i:(i+kscn-1),j:(j+kscm-1)] <- mark[i:(i+kscn-1),j:(j+kscm-1)]+ksc - - } - - } - - mark <- tobin(mark) - - mark - -} - -arealtest <- function(wt,dt=1,s0=1,noctave=5,nvoice=20,w0=2*pi,swabs=0,tw=0,siglevel,arealsiglevel=0.9,kernel=0,type=0){ - - slp <- slope(w0,swabs,tw,nvoice,siglevel,arealsiglevel,type) - - if (length(kernel)<2){ - maxarea <- s0*2^noctave*slp/10*nvoice/dt - cat(paste("# calculating kernel (maxarea=",maxarea,")\n",sep="")) - cvkernel <- - kernelRoot(s0,w0,a=maxarea,noctave,nvoice,swabs,tw,dt) - - cat("# building kernel bitmap \n") - kernel <- - kernelBitmap(cvkernel,s0,w0,noctave,nvoice,swabs,tw,dt) - - } - - cat("# performing arealtest \n") - sl <- slide(wt,kernel,s0,noctave,nvoice,1) - - list(at=sl,kernel=kernel) - -} - -############################# -# PLOTTING -############################# - -plotat <- function(t,wt,at,sigplot){ - - if (length(at)>1){ - linewidth <- 1 - if (sigplot==3) - linewidth <- 5 - - contour(x=t,z=at,levels=0.5,add=TRUE,drawlabels=FALSE,axes=FALSE,lwd=linewidth,col="black") - } - -} - - -plotcv <- function(t,wt,cv){ - - if (length(dim(cv))==0) - - contour(x=t,z=wt,levels=c(cv),drawlabels=FALSE,axes=FALSE,add=TRUE,col="black",lwd=1) - - else{ - - for(i in 1:nrow(cv)){ - - v <- cv[i,] - v[v==0] <- 9999 - m <- matrix(rep(v,length(t)),nrow=length(t),byrow=TRUE) - - contour(x=t,z=wt/m,levels=1,drawlabels=FALSE,axes=FALSE,add=TRUE,col="black",lwd=1) - - } - - } - -} - -plotcoi <- function(t,s0,noctave,w0){ - - tv <- as.vector(t) - tvl <- tv[tv-tv[1]<(tv[length(tv)]-tv[1])/2] - tvr <- tv[tv-tv[1]>=(tv[length(tv)]-tv[1])/2] - - lines(tvl,log2(((tvl-tv[1])*4*pi/((w0+sqrt(2+w0*w0))*sqrt(2)))/s0)/noctave,col="black") - lines(tvr,log2(((tv[length(tv)]-tvr)*4*pi/((w0+sqrt(2+w0*w0))*sqrt(2)))/s0)/noctave,col="black") - -} - -plotmarks <- function(t,s0,noctave,markt,marks){ - - if (markt[1]!=-999){ - - for (i in 1:length(markt)){ - lines(c(markt[i],markt[i]),c(0,1),lty="dotted") - } - - } - - if (marks[1]!=-999){ - - for (i in 1:length(marks)){ - lines(c(t[1],t[length(t)]),c(log2(marks[i]/s0)/noctave,log2(marks[i]/s0)/noctave),lty="dotted") - } - - } - -} - - -##################### -# PLOT.WT -##################### - -plot.wt <- function(wt,markt=-999,marks=-999,t1=NULL,t2=NULL,logscale=FALSE,phase=FALSE,units="",plottitle="",device="screen",file="wt",split=FALSE,color=TRUE,pwidth=10,pheight=5,labsc=1.5,labtext="",sigplot=3,xax=NULL,xlab=NULL,yax=NULL,ylab=NULL){ - - plotwt(wt$modulus,wt$phase,wt$time,wt$s0,wt$noctave,wt$w0,wt$critval,wt$at,markt,marks,t1,t2,logscale,phase,units,plottitle,device,file,split,color,pwidth,pheight,labsc,labtext,sigplot,xax,xlab,yax,ylab) - -} - -plotwt <- - function(wt,phs,t,s0,noctave,w0,cv=NULL,at=NULL,markt=-999,marks=-999,t1=NULL,t2=NULL,logscale=FALSE,phase=FALSE,units="",plottitle="Wavelet Plot",device="screen",file="wavelet",split=FALSE,color=TRUE,pwidth=10,pheight=7,labsc=1,labtext="",sigplot=1,xax=NULL,xlab=NULL,yax=NULL,ylab=NULL){ - - if (is.null(phs)) phase <- FALSE - - mgpv <- c(3,1,0) - if (labsc>1) mgpv[1] <- 3-(labsc-1.5) - - ncolors <- 64 - colors <- wtcolors(ncolors) - if (color==FALSE) colors <- gray((0:ncolors)/ncolors*0.7+0.3) - - rangev <- (0:(ncolors-1))/(ncolors-1) - rangebar <- matrix(rangev,nrow=2,ncol=64,byrow=TRUE) - - s <- 2^(0:(noctave))*s0 - sn <- (0:(noctave))/noctave - - deltat <- (t[length(t)]-t[1])/(length(t)-1) - cut <- FALSE - if ((!is.null(t1)) | (!is.null(t2))){ - if (t1<t2 & t1>=t[1] & t2<=t[length(t)]){ - - cut <- TRUE - - i1 <- (t1-t[1])/deltat+1 - i2 <- (t2-t[1])/deltat+1 - - t <- t[t>=t1 & t<=t2] - - wt <- wt[i1:i2,] - if (phase) phs <- phs[i1:i2,] - if (length(at)>1) at <- at[i1:i2,] - - } - } - - #---------------- - # Setting layout - #---------------- - - if (device=="ps" && split==FALSE){ - - file <- paste(file,".eps",sep="") - - postscript(file,onefile=TRUE,horizontal=TRUE,paper="special",width=pwidth,height=pheight) - cat(paste("# writing",file," \n")) - - } - - if (device=="ps" && split==TRUE){ - - file1 <- paste(file,".mod.eps",sep="") - - postscript(file1,onefile=TRUE,horizontal=TRUE,paper="special",width=pwidth,height=pheight) - cat(paste("# writing",file1," \n")) - - } - - if (phase==TRUE && split==FALSE) nlo <- layout(matrix(c(1,2,3,4),2,2,byrow=TRUE),widths=c(4,1)) - else nlo <- layout(matrix(c(1,2),ncol=2,byrow=TRUE),widths=c(4,1)) - - - #----------------- - # Plotting modulus - #----------------- - - if (logscale){ - if (units=="") - image(x=t,z=log10(wt),col = colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - else - image(x=t,z=log10(wt),col = colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - } - else{ - if (units=="") - image(x=t,z=wt,col = colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - else - image(x=t,z=wt,col = colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - } - - text(t[1+as.integer(length(t)*0.1)],0.8,labtext,cex=2) - - if (sigplot==1 | sigplot==3){ - if (is.null(cv)==FALSE){ #Critical values - if (cv[1]!=0 & cv[1]!=-1) plotcv(t,wt,cv) - } - } - - if (sigplot>1) plotat(t,wt,at,sigplot) - - if (!cut) plotcoi(t,s0,noctave,w0) #cone of influence - plotmarks(t,s0,noctave,markt,marks) #additional guiding lines - - if (is.null(xax)) - axis(side=1,at=axTicks(1),cex.axis=labsc) - else - if (is.null(xlab)) - axis(side=1,xax,labels=as.character(xax),cex.axis=labsc) - else - axis(side=1,xax,labels=xlab,cex.axis=labsc) - - if (is.null(yax)) - axis(side=2,sn,labels=as.character(s),cex.axis=labsc) - else - if (is.null(ylab)) - axis(side=2,yax,labels=as.character(yax),cex.axis=labsc) - else - axis(side=2,yax,labels=ylab,cex.axis=labsc) - - title(main=plottitle,cex=labsc) - - image(z=rangebar,axes=FALSE,col=colors,frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - - if (is.null(cv)==FALSE){ - if (length(dim(cv))==0){ - for (i in 1:length(cv)) - if (cv[i]!=0) lines(c(-1,2),c(cv[i],cv[i])) - } - } - - if (!logscale) - axis(side=2,(0:5)/5,labels=c("0","","","","","1"),cex.axis=labsc) - else{ - labelv <- substr(as.character(c(0:5)*(max(log10(wt),na.rm=TRUE)-min(log10(wt),na.rm=TRUE))/5),1,4) - axis(side=2,(0:5)/5,labels=labelv,cex.axis=labsc) - } - - - #----------------- - # Plotting phase - #----------------- - if (phase==TRUE){ - - if (device=="ps" && split==TRUE){ - - dev.off() - - file2 <- paste(file,".phs.eps",sep="") - - postscript(file2,onefile=TRUE,horizontal=TRUE,paper="special",width=10,height=5) - cat(paste("# writing",file2," \n")) - - } - - colors <- rainbow(ncolors) - if (color==FALSE){ - dummy <- gray((0:ncolors)/ncolors) - colors[1:(ncolors/2)] <- dummy[(ncolors/2+1):ncolors] - colors[(ncolors/2+1):ncolors] <- dummy[1:(ncolors/2)] - } - - if (units=="") - image(x=t,z=phs,col=colors,axes=FALSE,xlab="Time",ylab="Scale",frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - else - image(x=t,z=phs,col=colors,axes=FALSE,xlab=paste("Time ","[",units,"]",sep=""),ylab=paste("Scale ","[",units,"]",sep=""),frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - - if (is.null(cv)==FALSE) plotcv(t,wt,cv) - if (sigplot>1) plotat(t,wt,at,sigplot) - if (!cut) plotcoi(t,s0,noctave,w0) - plotmarks(t,s0,noctave,markt,marks) - - if (is.null(xax)) - axis(side=1,at=axTicks(1),cex.axis=labsc) - else - if (is.null(xlab)) - axis(side=1,xax,labels=as.character(xax),cex.axis=labsc) - else - axis(side=1,xax,labels=xlab,cex.axis=labsc) - - if (is.null(yax)) - axis(side=2,sn,labels=as.character(s),cex.axis=labsc) - else - if (is.null(ylab)) - axis(side=2,yax,labels=as.character(yax),cex.axis=labsc) - else - axis(side=2,yax,labels=ylab,cex.axis=labsc) - - - title(main="Phase") - - image(z=rangebar,axes=FALSE,col=colors,frame.plot=TRUE,cex.lab=labsc,mgp=mgpv) - axis(side=2,(0:4)/4,labels=c("-PI","","0","","PI"),cex.axis=labsc) - - } - - if (device=="ps") dev.off() - - } - -############################## -# Surrogates -############################## - -createwavesurrogates <- function(nsurr=1,wt=1,n,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi){ - - surrmat <- matrix(rep(0,n*nsurr),ncol=nsurr) - - for (i in 1:nsurr){ - - cat(paste("# Creating realization ",i,"\n",sep="")) - - x <- rnorm(n) - xts <- ts(x,start=0,deltat=dt) - - xwt <- cwt.ts(xts,s0,noctave,nvoice,w0) - wtsurr <- wt*xwt - - surri <- as.vector(invmorlet(wtsurr,0,dt,s0,noctave,nvoice,w0)) - - surrmat[,i] <- Re(surri) - - } - - surrmat - -} - -surrwave <- function(x,...) - UseMethod("surrwave") - -surrwave.wt <- function(wt,nsurr=1,spec=TRUE){ - - n <- length(wt$time) - t0 <- wt$time[1] - dt <- (wt$time[13]-t0)/12 - s0 <- wt$s0 - noctave <- wt$noctave - nvoice <- wt$nvoice - w0 <- wt$w0 - - wt <- wt$modulus - if (spec==TRUE) - wt <- sqrt(wt) - - surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) - - surrts <- ts(surrmat,start=t0,deltat=dt) - - surrts - -} - -surrwave.matrix <- function(mat,nsurr=1,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,spec=FALSE){ - - if (sw!=0 & swabs==0) - swabs <- as.integer(sw*nvoice) - - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - - if ((noctave*nvoice+1)!=dim(mat)[2]) - cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n") - - n <- dim(mat)[1] - - if (spec==FALSE) - mat <- Mod(mat) - else - mat <- sqrt(Mod(mat)) - - wtsm <- smooth.matrix(mat,swabs) - wt <- smooth.time(wtsm,tw,dt,scalevector) - - surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) - - surrts <- ts(surrmat,start=t0,deltat=dt) - - surrts - -} - -surrwave.character <- - function(file,nsurr=1,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0,transpose=TRUE,spec=FALSE){ - - if (sw!=0 & swabs==0) - swabs <- as.integer(sw*nvoice) - - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - - if (transpose==FALSE) - mat <- matrix(scan(file,comment.char="#"),ncol=nvoice*noctave+1,byrow=TRUE) - else - mat <- matrix(scan(file,comment.char="#"),ncol=nvoice*noctave+1,byrow=FALSE) - - if ((noctave*nvoice+1)!=dim(mat)[2]) - cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n") - - n <- dim(mat)[1] - - if (spec==FALSE) - mat <- Mod(mat) - else - mat <- sqrt(Mod(mat)) - - wtsm <- smooth.matrix(mat,swabs) - wt <- smooth.time(wtsm,tw,dt,scalevector) - - surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) - - surrts <- ts(surrmat,start=t0,deltat=dt) - - surrts - - } - -surrwave.ts <- function(ts,nsurr=1,s0=1,noctave=5,nvoice=10,w0=2*pi,sw=0,tw=0,swabs=0){ - - n <- length(ts) - t0 <- time(ts)[1] - dt <- deltat(ts) - if (sw!=0 & swabs==0) - swabs <- as.integer(sw*nvoice) - - scalevector <- 2^(0:(noctave*nvoice)/nvoice)*s0 - - wt <- Mod(cwt.ts(ts,s0,noctave,nvoice,w0)) - - wtsm <- smooth.matrix(wt,swabs) - wt <- smooth.time(wtsm,tw,dt,scalevector) - - surrmat <- createwavesurrogates(nsurr,wt,n,dt,s0,noctave,nvoice,w0) - - surrts <- ts(surrmat,start=t0,deltat=dt) - - surrts - -} - -invmorlet <- function(wt,t0=0,dt=1,s0=1,noctave=5,nvoice=10,w0=2*pi){ - - if ((noctave*nvoice+1)!=dim(wt)[2]) - cat("# ERROR! nscales unequal noctave*nvoice+1 ! \n") - - n <- dim(wt)[1] - - wt[is.na(wt)] <- 0 - - tsRe <- rep(0,n) - tsIm <- rep(0,n) - - wtRe <- t(Re(wt)) - wtIm <- t(Im(wt)) - - z <- .C("invmorlet", - as.double(as.vector(wtRe)), - as.double(as.vector(wtIm)), - as.integer(n), - as.double(dt), - as.double(s0), - as.integer(noctave), - as.integer(nvoice), - as.double(w0), - tsRetmp = as.double(tsRe), - tsImtmp = as.double(tsIm), - PACKAGE="sowas") - - invvec=complex(real=z$tsRetmp,imaginary=z$tsImtmp) - invts <- ts(data=invvec,start=t0,deltat=dt) - - invts - -} - -################################# -# INPUT / OUTPUT -################################# - -readmatrix <- function(file,M){ - - A <- matrix(scan(file,comment.char="#"),ncol=M,byrow=TRUE) - - A - -} - - -readts <- function(file){ - - A <- matrix(scan(file,comment.char="#"),ncol=2,byrow=TRUE) - - Adum <- A - - Adum[is.na(Adum)] <- 0 - - t <- Adum %*% c(1,0) - x <- A %*% c(0,1) - - N=length(t) - f=1/(t[13]-t[1])*12 - - if ((f>11) && (f<13)) f <- 12 - - timeseries<-ts(data=x,start=t[1],frequency=f) - - timeseries - -} - -writematrix <- function(file,data,header="# R Matrix"){ - - write(header,file) - write(t(data),file,ncol(data),append=TRUE) - -} - -############################ -# HELP FUNCTIONS -############################ - -smooth.matrix <- function(wt,swabs){ - - if (swabs != 0) - smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1))) - else - smwt <- wt - - smwt - -} - -smooth.time <- function(wt,tw,dt,scalevector){ - - smwt <- wt - - if (tw != 0){ - for (i in 1:length(scalevector)){ - - twi <- as.integer(scalevector[i]*tw/dt) - smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1)) - - } - } - - smwt - -} - - -adjust.noctave <- function(N,dt,s0,tw,noctave){ - - if (tw>0){ - dumno <- as.integer((log(N*dt)-log(2*tw*s0))/log(2)) - if (dumno<noctave){ - cat("# noctave adjusted to time smoothing window \n") - noctave <- dumno - } - } - - noctave - -} - -adjust.s0 <- function(s0,dt){ - - if (s0<2*dt){ - s0 <- 2*dt - cat(paste("# s0 set to ",s0," \n")) - } - - s0 - -} - -adjust.timeseries <- function(ts1,ts2){ - - if (length(ts1)!=length(ts2)){ - tsint <- ts.intersect(ts1,ts2) - dt <- deltat(ts1) - ts1 <- ts(data=tsint[,1],start=time(tsint)[1],frequency=1/dt) - ts2 <- ts(data=tsint[,2],start=time(tsint)[1],frequency=1/dt) - t <- time(ts1) - } - - list(ts1=ts1,ts2=ts2) - -} - -checkarealsiglevel <- function(sw,tw,w0,arealsiglevel,siglevel,type){ - - if (type==0){ - - swv <- c(0,0.5,1) - twv <- c(0,1.5,3) - w0v <- c(pi,2*pi) - - if (length(swv[swv==sw])==0 || length(twv[twv==tw])==0 || - length(w0v[w0v==w0])==0){ - arealsiglevel <- 0 - cat("# areawise test for spectrum currently \n") - cat("# only possible for \n") - cat("# sw = 0 \n") - cat("# tw = 0 \n") - cat("# w0 = 2pi \n") - cat("# No areawise test performed \n") - } - } - - if (type==1){ - - swv <- c(0.5) - twv <- c(1.5) - w0v <- c(2*pi) - - if (length(swv[swv==sw])==0 || length(twv[twv==tw])==0 || - length(w0v[w0v==w0])==0){ - arealsiglevel <- 0 - cat("# areawise test for coherence currently \n") - cat("# only possible for \n") - cat("# sw = 0.5 \n") - cat("# tw = 1.5 \n") - cat("# w0 = 2pi \n") - cat("# No areawise test performed \n") - } - } - - - if (arealsiglevel!=0){ - arealsiglevel <- 0.9 - siglevel <- 0.95 - cat("# currently only siglevel=0.95 and arealsiglevel=0.9 possible for areawise test \n") - } - - list(siglevel=siglevel,arealsiglevel=arealsiglevel) - -} - -######################## - -as.wt <- function(modulus,phase=NULL,s0=NULL,noctave=NULL,nvoice=NULL,w0=NULL,dt=1,time=NULL,scales=NULL,critval=NULL,at=NULL,kernel=NULL,N=NULL,t0=NULL){ - - if (is.null(scales)) - gotscales <- FALSE - else - gotscales <- TRUE - - if ((!gotscales) & (!is.null(s0)) & (!is.null(noctave)) &(!is.null(nvoice))){ - gotscales <- TRUE - scales=2^(0:(noctave*nvoice)/nvoice)*s0 - } - - if (gotscales & (is.null(s0) | is.null(noctave) | - is.null(nvoice))){ - s0 <- scales[1] - noctave <- log(scales[length(scales)]/s0)/log(2) - nvoice <- (length(scales)-1)/noctave - } - - - if (!gotscales) - cat("# ERROR! No scales given! \n") - - if (is.null(time)) gottimes <- FALSE - else gottimes <- TRUE - - if ((!gottimes) & (!is.null(dt)) & (!is.null(t0)) &(!is.null(N))){ - gottimes <- TRUE - time=(0:(N-1))*dt+t0 - } - - if (!gottimes) - cat("# ERROR! No time vector given! \n") - - wcolist <- list(modulus=modulus,phase=phase,s0=s0,noctave=noctave,nvoice=nvoice,w0=w0,time=time,scales=scales,critval=critval,at=at,kernel=kernel) - - class(wcolist) <- "wt" - - wcolist - -} - -######################## - -wtcolors <- function(ncolors){ - - upside <- rainbow(ncolors,start=0,end=.7) - #upside <- heat.colors(ncolors+5) - #upside <- upside[1:ncolors] - - - down <- upside - - for (i in 1:ncolors){ - down[i] <- upside[ncolors+1-i] - }# - - down - -} - -#################### - -createwgn <- function(N,sig,dt){ - - timeseries<-ts(data=rnorm(N,0,sig),start=0,deltat=dt) - - timeseries - -} - - -createar <- function(N,a,sig,dt){ - - if (a==0) a <- 0.000000001 - - se <- sqrt(sig*sig*(1-a*a)) - tsMC <- ts(data=rep(0,N),start=0,deltat=dt) - - tsMC[1:N] <- arima.sim(list(ar = a), N, sd = se) - -} - -###################### - -rk <- function(N=1000,s=8,noctave=5,nvoice=10,w0=2*pi,plot=TRUE){ - - t <- 1:N - - sunit <- s*(w0+sqrt(2+w0*w0))/(4*pi) - - s0 <- 4 - #s0unit <- s0*(w0+sqrt(2+w0*w0))/(4*pi) - s0unit=s0/dt*w0/(2*pi) #(CORRECT) - s0log <- as.integer((log2(s0unit)-1)*nvoice+1.5) - - totnoct <- noctave+as.integer(s0log/nvoice)+1 - - x <- morlet(N,N/2,sunit,w0) - - totts.cwt <- Mod(cwt(x,totnoct,nvoice,w0,plot=0)) - wt=totts.cwt[,s0log:(s0log+noctave*nvoice)] - - wt <- wt/max(wt) - - if (plot==TRUE) plotwt(wt,0,t,s0,noctave,w0,units="",plottitle="Reproducing Kernel") - - wt - -} - -################### - -addvalues <- function(nvoice,dnoctave,x,value){ - - nr <- dim(x)[1] - nc <- dim(x)[2] - dnc <- nvoice*dnoctave - - y <- matrix(rep(value,nr*(nc+dnc)),nrow=nr) - - y[,(dnc+1):(dnc+nc)] <- x - - y - -} - -#################### - -scalematrix <- function(wt){ - - # scales matrix, such that the maximal value is one - # wt: matrix to be scaled - - mval <- max(wt,na.rm=TRUE) - - wt <- wt/mval - - wt - -} - - -foldKernel <- function(F,swabs,tw,s,dt){ - - # folds a matrix (e.g. kernel with smoothing window - # F: matrix input - # swabs: smooting window width - - smsF <- smooth.matrix(F,swabs) - smsF[is.na(smsF)] <- 0 - - smtsF <- smooth.time(smsF,tw,dt,s) - - smtsF[is.na(smtsF)] <- 0 - - scF <- scalematrix(smtsF) - - scF - -} - -kernelBitmap <- function(c,s0=1,w0=6,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){ - # produces kernel bitmap - # c: height of contour, that defines area - # s0: lowest scale - # noctave: number of octaves - # nvoice: number of voices per octave - # swabs: smoothing window length in scale direction - # dt: sampling time - - s <- s0*2^noctave - is <- noctave*nvoice - - x <- s0*2^(((1:(nvoice*(noctave+2)))-1)/nvoice) - - t <- max(2000,max(x)*tw*2/dt*1.1) - - y <- ((0:(2*t))-t)/2*dt - - X <- matrix(x,ncol=nvoice*(noctave+2),nrow=2*t+1,byrow=T) - Y <- matrix(y,ncol=nvoice*(noctave+2),nrow=2*t+1) - - F <- sqrt(2*s*X/(s*s+X*X))*exp(-0.5*(Y*Y+w0*w0*(X-s)*(X-s))/(s*s+X*X)) - - F <- foldKernel(F,swabs,tw,x,dt) - - F[F<c] <- 0 - F[F>=c] <- 1 - - is1 <- 1 - is2 <- nvoice*(noctave+1) - it1 <- 1 - it2 <- 2*t+1 - - L <- F[1:(2*t+1),is1] - - while (length(L[L!=0])==0) { - is1 <- is1+1 - L <- F[1:(2*t+1),is1] - } - - L <- F[1:(2*t+1),is2] - - while (length(L[L!=0])==0) { - is2 <- is2-1 - L <- F[1:(2*t+1),is2] - } - - - L <- F[it1,1:(nvoice*(noctave+2))] - - while (length(L[L!=0])==0) { - it1 <- it1+1 - L <- F[it1,1:(nvoice*(noctave+2))] - } - - L <- F[it2,1:(nvoice*(noctave+2))] - - while (length(L[L!=0])==0) { - it2 <- it2-1 - L <- F[it2,1:(nvoice*(noctave+2))] - } - - kernel <- list(bitmap=F[(it1-1):(it2+1),(is1-1):(is2+1)],is=is-is1) - - kernel - -} - -kernelRoot <- function(s0=1,w0=6,a=0,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){ - - tol <- 0.005 - cmin <- 0 - cmax <- 1 - cntr <- 0.5 - da <- a - - while (abs(da/a)>tol){ - - da <- kernelArea(cntr,s0,w0,a,noctave,nvoice,swabs,tw,dt) - if (da>0){ - cmin <- cntr - cntr <- mean(c(cntr,cmax)) - } - if (da<0){ - cmax <- cntr - cntr <- mean(c(cntr,cmin)) - } - } - - cntr - -} - -kernelArea <- function(cntr,s0=1,w0=6,a=0,noctave=6,nvoice=20,swabs=0,tw=0,dt=1){ - - # calulates area of reproducing kernel for smoothed data at scale s0*2^noctave - # cntr: height of contour line to define kernel area. This - # parameter is to be estimated! - # s0: lowest scale - # w0: parameter of Morlet Wavelet - # a: area offset (only needed, when finding root. Is set to - # desired area - # noctave: number of octaves - # nvoice: number of voices per octave - # swabs: smoothing window width in scale direction - # dt: sampling time - - s <- s0*2^noctave - - x <- s0*2^(((1:(nvoice*(noctave+2)))-1)/nvoice) - - t <- max(2000,max(x)*tw*2/dt*1.1) - - y <- ((0:(2*t))-t)/2*dt - - X <- matrix(x,ncol=nvoice*(noctave+2),nrow=2*t+1,byrow=T) - Y <- matrix(y,ncol=nvoice*(noctave+2),nrow=2*t+1) - - F <- sqrt(2*s*X/(s*s+X*X))*exp(-0.5*(Y*Y+w0*w0*(X-s)*(X-s))/(s*s+X*X)) - - F <- foldKernel(F,swabs,tw,x,dt) - - F[F>=cntr] <- 1 - F[F<cntr] <- 0 - - area <- length(F[F==1])-a - - area - -} - - -tobin <- function(x){ - - # sets nonzero values to one - - y <- x/x - y[is.na(y)] <- 0 - - y - -} - -scaleKernel <- function(kernel,l){ - - # scales kernel length in time direction proportional to scale - # kernel: data bitmap of width n - # l: new width of kernel - - n <- nrow(kernel) - m <- ncol(kernel) - - newKernel <- matrix(rep(0,m*l),nrow=l) - - d <- as.double(n)/as.double(l) - - for (i in 1:l){ - j <- as.integer((i-0.5)*d) - if (j==0) j <- 1 - newKernel[i,1:m] <- kernel[j,1:m] - } - - newKernel - -} - -slope <- function(w0,swabs,tw,nvoice,siglevel,arealsiglevel,type){ - - sw <- swabs/nvoice - - if (type==0){ # wavelet spectrum - - if (tw == 0 & sw == 0 & w0 == 1 *pi) slp <- 5.82518 # w = 18.35831 - if (tw == 1.5 & sw == 0 & w0 == 1 *pi) slp <- 24.69852 # w = 14.30709 - if (tw == 3 & sw == 0 & w0 == 1 *pi) slp <- 35.48368 # w = 14.72354 - if (tw == 0 & sw == 5 & w0 == 1 *pi) slp <- 7.347707 # w = 17.96942 - if (tw == 1.5 & sw == 5 & w0 == 1 *pi) slp <- 28.24291 # w = 12.65993 - if (tw == 3 & sw == 5 & w0 == 1 *pi) slp <- 51.13723 # w = 10.96359 - if (tw == 0 & sw == 10 & w0 == 1 *pi) slp <- 10.47856 # w = 15.5941 - if (tw == 1.5 & sw == 10 & w0 == 1 *pi) slp <- 45.07387 # w = 15.29793 - if (tw == 3 & sw == 10 & w0 == 1 *pi) slp <- 52.82886 # w = 12.72361 - - if (tw == 0 & sw == 0 & w0 == 2 *pi) slp <- 8.718912 # w = 17.75933 - if (tw == 1.5 & sw == 0 & w0 == 2 *pi) slp <- 11.88006 # w = 15.39648 - if (tw == 3 & sw == 0 & w0 == 2 *pi) slp <- 26.55977 # w = 1.064384 - if (tw == 0 & sw == 5 & w0 == 2 *pi) slp <- 14.64761 # w = 16.27518 - if (tw == 1.5 & sw == 5 & w0 == 2 *pi) slp <- 28.27798 # w = 14.57059 - if (tw == 3 & sw == 5 & w0 == 2 *pi) slp <- 63.54121 # w = 12.83778 - if (tw == 0 & sw == 10 & w0 == 2 *pi) slp <- 27.78735 # w = 11.95813 - if (tw == 1.5 & sw == 10 & w0 == 2 *pi) slp <- 41.27260 # w = 12.03379 - if (tw == 3 & sw == 10 & w0 == 2 *pi) slp <- 67.37015 # w = 10.63935 - - } - - if (type==1){ # wavelet coherence - - if (tw==0 & sw==0 & w0==pi) slp <- 999 #not valid - if (tw==1.5 & sw==0 & w0==pi) slp <- 1 - if (tw==3 & sw==0 & w0==pi) slp <- 1 - if (tw==0 & sw==0.5 & w0==pi) slp <- 1 - if (tw==1.5 & sw==0.5 & w0==pi) slp <- 1 - if (tw==3 & sw==0.5 & w0==pi) slp <- 1 - if (tw==0 & sw==1 & w0==pi) slp <- 1 - if (tw==1.5 & sw==1 & w0==pi) slp <- 1 - if (tw==3 & sw==1 & w0==pi) slp <- 1 - - if (tw==0 & sw==0 & w0==2*pi) slp <- 999 #not valid - if (tw==1.5 & sw==0 & w0==2*pi) slp <- 1 - if (tw==3 & sw==0 & w0==2*pi) slp <- 1 - if (tw==0 & sw==0.5 & w0==2*pi) slp <- 1 - if (tw==1.5 & sw==0.5 & w0==2*pi) slp <- 8.3 - if (tw==3 & sw==0.5 & w0==2*pi) slp <- 1 - if (tw==0 & sw==1 & w0==2*pi) slp <- 1 - if (tw==1.5 & sw==1 & w0==2*pi) slp <- 1 - if (tw==3 & sw==1 & w0==2*pi) slp <- 1 - - if (tw==0 & sw==0 & w0==3*pi) slp <- 999 #not valid - if (tw==1.5 & sw==0 & w0==3*pi) slp <- 1 - if (tw==3 & sw==0 & w0==3*pi) slp <- 1 - if (tw==0 & sw==0.5 & w0==3*pi) slp <- 1 - if (tw==1.5 & sw==0.5 & w0==3*pi) slp <- 1 - if (tw==3 & sw==0.5 & w0==3*pi) slp <- 1 - if (tw==0 & sw==1 & w0==3*pi) slp <- 1 - if (tw==1.5 & sw==1 & w0==3*pi) slp <- 1 - if (tw==3 & sw==1 & w0==3*pi) slp <- 1 - - if (tw==0 & sw==0 & w0==4*pi) slp <- 999 #not valid - if (tw==1.5 & sw==0 & w0==4*pi) slp <- 1 - if (tw==3 & sw==0 & w0==4*pi) slp <- 1 - if (tw==0 & sw==0.5 & w0==4*pi) slp <- 1 - if (tw==1.5 & sw==0.5 & w0==4*pi) slp <- 1 - if (tw==3 & sw==0.5 & w0==4*pi) slp <- 1 - if (tw==0 & sw==1 & w0==4*pi) slp <- 1 - if (tw==1.5 & sw==1 & w0==4*pi) slp <- 1 - if (tw==3 & sw==1 & w0==4*pi) slp <- 1 - - } - - cat(paste("# slope ",slp,"\n",sep="")) - - slp - -} diff --git a/old_C_code/stage2_UNFINISHED/test/.gitkeep b/old_C_code/stage2_UNFINISHED/test/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/old_C_code/wrapper.R b/old_C_code/wrapper.R deleted file mode 100644 index d6d05e1..0000000 --- a/old_C_code/wrapper.R +++ /dev/null @@ -1,74 +0,0 @@ -#Exemple : -# -#dans old_C_code/build : -#cmake ../stage1/src -#make -# -#dans data/, lancer R puis : -#source("../old_C_code/wrapper.R") -#serialize("../old_C_code/build", "2009.csv","2009.bin",1) -#library(parallel) -#np = detectCores() -#nbSeriesPerChunk = 3000 -#nbClusters = 20 -#ppam_exe("../old_C_code/build",np,"2009.bin",nbSeriesPerChunk,nbClusters) -#C = getMedoids("../old_C_code/build", "ppamResult.xml", "ppamFinalSeries.bin") -#first100series = deserialize("../old_C_code/build", "2009.bin", "2009.csv.part", "1-100") -#distor = getDistor("../old_C_code/build", "ppamResult.xml", "2009.bin") - -ppam_exe = function(path=".", np=parallel::detectCores(), data=NULL, - nbSeriesPerChunk, nbClusters, randomize=1, p_dissims=2) -{ - args = paste(nbSeriesPerChunk," ",nbClusters," ",randomize," ",p_dissims,sep="") - - command_line = paste("mpirun -np ",np," ",path,"/ppam.exe cluster",sep="") - - #if data provided (as data.frame or matrix...): binarize it, and add it as first argument - if (!is.null(data)) - { - if (!is.character(data)) - { - #assuming matrix or data.frame, WITH row names - #( identifiers; could be line number... e.g. data <- cbind(1:nrow(data),data) ) - write.table(data, "/tmp/data_csv", sep=",", row.names=FALSE, col.names=FALSE) - system(paste(path,"/ppam.exe serialize /tmp/data_csv /tmp/data_bin 0 0",sep="")) - } else - { - system(paste(path,"/ppam.exe serialize ",data," /tmp/data_bin 0 0",sep="")) - } - command_line = paste(command_line," /tmp/data_bin",sep="") - } - - command_line = paste(command_line," ",args,sep="") - system(command_line) -} - -#NOTE: identifiers in first column -getMedoids = function(path=".", xmlResult = "ppamResult.xml", - finalSeries = "ppamFinalSeries.bin") -{ - system(paste(path,"/ppam.exe deserialize ",finalSeries," ppamFinalSeries.csv -1",sep="")) - curves = read.table("ppamFinalSeries.csv", sep=",") - library(XML) - ranks = as.integer( xmlToList( xmlParse(xmlResult) )$ranks ) - return ( curves[ranks,] ) # == medoids -} - -getDistor = function(path=".", xmlResult = "ppamResult.xml", - finalSeries = "ppamFinalSeries.bin") -{ - system(paste(path,"/ppam.exe classif ",finalSeries," ",xmlResult,sep="")) -} - -serialize = function(path=".", csvSeries, binSeries, byCols=0, nbSeries=0) -{ - system(paste(path,"/ppam.exe serialize ",csvSeries," ",binSeries," ",byCols," ",nbSeries, - sep="")) -} - -deserialize = function(path=".", binSeries, csvSeries, ranks="-1", return=TRUE) -{ - system(paste(path,"/ppam.exe deserialize ",binSeries," ",csvSeries," ",ranks,sep="")) - if (return) - return ( read.table(csvSeries, sep=",") ) -} diff --git a/reports/2017-05/TODO b/reports/2017-03/TODO similarity index 100% rename from reports/2017-05/TODO rename to reports/2017-03/TODO